Merge branch 'develop' into amd-develop

CMakeLists.txt

@@ -23,7 +23,7 @@ endif()
 
 set(version 1.1.0)
 # Check support for CUDA/HIP in Cmake
-project(composable_kernel VERSION ${version} LANGUAGES CXX)
+project(composable_kernel VERSION ${version} LANGUAGES CXX HIP)
 include(CTest)
 
 find_package(Python3 3.6 COMPONENTS Interpreter REQUIRED)
@@ -112,7 +112,7 @@ message("checking which targets are supported")
 #Setting GPU_TARGETS on command line will override this list
 if(NOT PROFILER_ONLY)
     rocm_check_target_ids(DEFAULT_GPU_TARGETS
-        TARGETS "gfx908;gfx90a;gfx940;gfx941;gfx942;gfx1030;gfx1100;gfx1101;gfx1102")
+        TARGETS "gfx900;gfx906;gfx908;gfx90a;gfx940;gfx941;gfx942;gfx1030;gfx1100;gfx1101;gfx1102")
 else()
     add_definitions(-DPROFILER_ONLY)
     set(GPU_TARGETS "" CACHE STRING "" FORCE)
@@ -135,12 +135,10 @@ endif()
 
 message("Supported GPU_TARGETS= ${DEFAULT_GPU_TARGETS}")
 
-set(AMDGPU_TARGETS "${DEFAULT_GPU_TARGETS}" CACHE STRING " " FORCE)
-
 if(GPU_TARGETS)
     message("Building CK for the following targets: ${GPU_TARGETS}")
 else()
-    message("Building CK for the following targets: ${AMDGPU_TARGETS}")
+    message("Building CK for the default targets: ${DEFAULT_GPU_TARGETS}")
 endif()
 
 if (GPU_TARGETS)
@@ -225,7 +223,13 @@ link_libraries(Threads::Threads)
 set(CMAKE_CXX_STANDARD 17)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
 set(CMAKE_CXX_EXTENSIONS OFF)
-message("CMAKE_CXX_COMPILER_ID: ${CMAKE_CXX_COMPILER_ID}")
+message("CMAKE_CXX_COMPILER: ${CMAKE_CXX_COMPILER}")
+
+## HIP
+set(CMAKE_HIP_PLATFORM amd)
+set(CMAKE_HIP_COMPILER ${CMAKE_CXX_COMPILER})
+set(CMAKE_HIP_EXTENSIONS ON)
+message("CMAKE_HIP_COMPILER: ${CMAKE_HIP_COMPILER}")
 
 ## OpenMP
 if(CMAKE_CXX_COMPILER_ID MATCHES "Clang")

client_example/07_grouped_convnd_fwd/CMakeLists.txt

@@ -4,4 +4,22 @@ if(GPU_TARGETS MATCHES "gfx9")
 
     add_executable(client_grouped_conv1d_fwd grouped_conv1d_fwd.cpp)
     target_link_libraries(client_grouped_conv1d_fwd PRIVATE composable_kernel::device_conv_operations)
-endif()
+
\ No newline at end of file
+    if((DTYPES MATCHES "fp8") OR NOT DEFINED DTYPES)
+        add_executable(client_grouped_conv3d_fwd_fp8 grouped_conv3d_fwd_fp8.cpp)
+        target_link_libraries(client_grouped_conv3d_fwd_fp8 PRIVATE composable_kernel::device_conv_operations)
+    endif()
+
+    if((DTYPES MATCHES "bf8") OR NOT DEFINED DTYPES)
+        add_executable(client_grouped_conv3d_fwd_bf8 grouped_conv3d_fwd_bf8.cpp)
+        target_link_libraries(client_grouped_conv3d_fwd_bf8 PRIVATE composable_kernel::device_conv_operations)
+    endif()
+
+    if((DTYPES MATCHES "fp8" AND DTYPES MATCHES "bf8") OR NOT DEFINED DTYPES)
+        add_executable(client_grouped_conv3d_fwd_fp8_bf8 grouped_conv3d_fwd_fp8_bf8.cpp)
+        target_link_libraries(client_grouped_conv3d_fwd_fp8_bf8 PRIVATE composable_kernel::device_conv_operations)
+
+        add_executable(client_grouped_conv3d_fwd_bf8_fp8 grouped_conv3d_fwd_bf8_fp8.cpp)
+        target_link_libraries(client_grouped_conv3d_fwd_bf8_fp8 PRIVATE composable_kernel::device_conv_operations)
+    endif()
+endif()

client_example/07_grouped_convnd_fwd/common.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <cstdlib>
+#include <iomanip>
+#include <iostream>
+#include <iterator>
+#include <numeric>
+#include <string>
+#include <vector>
+
+#include "ck/ck.hpp"
+#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward.hpp"
+#include "ck/tensor_operation/gpu/device/device_grouped_conv_fwd_multiple_abd.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+struct SimpleDeviceMem
+{
+    SimpleDeviceMem() = delete;
+
+    SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
+    {
+        (void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
+    }
+
+    void* GetDeviceBuffer() { return p_mem_; }
+
+    ~SimpleDeviceMem() { (void)hipFree(p_mem_); }
+
+    void* p_mem_;
+};
+
+template <ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
+std::size_t
+GetFlops(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& output_lengths,
+         const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& weights_lengths)
+{
+    // 2 * G * N * K * C * <output spatial lengths product> * <filter spatial lengths product>
+    ck::index_t G = weights_lengths[0];
+    ck::index_t N = output_lengths[1];
+    ck::index_t K = weights_lengths[1];
+    ck::index_t C = weights_lengths[2];
+
+    return static_cast<std::size_t>(2) * G * N * K * C *
+           std::accumulate(std::next(std::begin(output_lengths), NumNonSpatialDim),
+                           std::end(output_lengths),
+                           static_cast<std::size_t>(1),
+                           std::multiplies<>()) *
+           std::accumulate(std::next(std::begin(weights_lengths), NumNonSpatialDim),
+                           std::end(weights_lengths),
+                           static_cast<std::size_t>(1),
+                           std::multiplies<>());
+}
+
+template <typename InDataType, ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
+std::size_t
+GetInputByte(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& input_lengths)
+{
+    // sizeof(InDataType) * (G * N * C * <input spatial lengths product>) +
+    return sizeof(InDataType) * std::accumulate(std::begin(input_lengths),
+                                                std::end(input_lengths),
+                                                static_cast<std::size_t>(1),
+                                                std::multiplies<>());
+}
+
+template <typename WeiDataType, ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
+std::size_t
+GetWeightByte(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& weights_lengths)
+{
+    // sizeof(WeiDataType) * (G * K * C * <filter spatial lengths product>) +
+    return sizeof(WeiDataType) * std::accumulate(std::begin(weights_lengths),
+                                                 std::end(weights_lengths),
+                                                 static_cast<std::size_t>(1),
+                                                 std::multiplies<>());
+}
+
+template <typename OutDataType, ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
+std::size_t
+GetOutputByte(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& output_lengths)
+{
+    // sizeof(OutDataType) * (G * N * K * <output spatial lengths product>);
+    return sizeof(OutDataType) * std::accumulate(std::begin(output_lengths),
+                                                 std::end(output_lengths),
+                                                 static_cast<std::size_t>(1),
+                                                 std::multiplies<std::size_t>());
+}
+
+template <ck::index_t NumDimSpatial,
+          typename InDataType,
+          typename WeiDataType,
+          typename OutDataType,
+          typename InLayout,
+          typename WeiLayout,
+          typename OutLayout,
+          ck::index_t NumNonSpatialDim = 3,
+          typename AComputeType        = InDataType,
+          typename BComputeType        = AComputeType>
+bool run_grouped_conv_fwd(std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> in_lengths,
+                          std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> wei_lengths,
+                          std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> out_lengths)
+{
+    std::size_t in_mem_size  = GetInputByte<InDataType, NumDimSpatial>(in_lengths);
+    std::size_t wei_mem_size = GetWeightByte<WeiDataType, NumDimSpatial>(wei_lengths);
+    std::size_t out_mem_size = GetOutputByte<OutDataType, NumDimSpatial>(out_lengths);
+
+    SimpleDeviceMem in(in_mem_size);
+    SimpleDeviceMem wei(wei_mem_size);
+    SimpleDeviceMem out(out_mem_size);
+
+    std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> in_strides;
+    std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> wei_strides;
+    std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> out_strides;
+    in_strides.fill(0);
+    wei_strides.fill(0);
+    out_strides.fill(0);
+    in_strides.back()  = 1;
+    wei_strides.back() = 1;
+    out_strides.back() = 1;
+
+    std::partial_sum(rbegin(in_lengths),
+                     std::prev(rend(in_lengths)),
+                     std::next(rbegin(in_strides)),
+                     std::multiplies<>{});
+    std::partial_sum(rbegin(wei_lengths),
+                     std::prev(rend(wei_lengths)),
+                     std::next(rbegin(wei_strides)),
+                     std::multiplies<>{});
+    std::partial_sum(rbegin(out_lengths),
+                     std::prev(rend(out_lengths)),
+                     std::next(rbegin(out_strides)),
+                     std::multiplies<>{});
+
+    // transpose NDHWGC/KZYXGC/NDHWGK to GNDHWC/GKZYXC/GNDHWK to GNCDHW/GKCZYX/GNKDHW
+    std::rotate(std::next(rbegin(in_lengths)), std::next(rbegin(in_lengths), 2), rend(in_lengths));
+    std::rotate(rbegin(in_lengths),
+                std::next(rbegin(in_lengths)),
+                std::next(rbegin(in_lengths), NumDimSpatial + 1));
+
+    std::rotate(std::next(rbegin(in_strides)), std::next(rbegin(in_strides), 2), rend(in_strides));
+    std::rotate(rbegin(in_strides),
+                std::next(rbegin(in_strides)),
+                std::next(rbegin(in_strides), NumDimSpatial + 1));
+
+    std::rotate(rbegin(wei_lengths),
+                std::next(rbegin(wei_lengths)),
+                std::next(rbegin(wei_lengths), NumDimSpatial + 1));
+
+    std::rotate(rbegin(wei_strides),
+                std::next(rbegin(wei_strides)),
+                std::next(rbegin(wei_strides), NumDimSpatial + 1));
+
+    std::rotate(
+        std::next(rbegin(out_lengths)), std::next(rbegin(out_lengths), 2), rend(out_lengths));
+    std::rotate(rbegin(out_lengths),
+                std::next(rbegin(out_lengths)),
+                std::next(rbegin(out_lengths), NumDimSpatial + 1));
+
+    std::rotate(
+        std::next(rbegin(out_strides)), std::next(rbegin(out_strides), 2), rend(out_strides));
+    std::rotate(rbegin(out_strides),
+                std::next(rbegin(out_strides)),
+                std::next(rbegin(out_strides), NumDimSpatial + 1));
+
+    std::array<ck::index_t, NumDimSpatial> conv_filter_strides;
+    std::array<ck::index_t, NumDimSpatial> conv_filter_dilations;
+    std::array<ck::index_t, NumDimSpatial> input_left_pads;
+    std::array<ck::index_t, NumDimSpatial> input_right_pads;
+    conv_filter_strides.fill(1);
+    conv_filter_dilations.fill(1);
+    input_left_pads.fill(1);
+    input_right_pads.fill(1);
+
+    std::size_t flop      = GetFlops<NumDimSpatial>(out_lengths, wei_lengths);
+    std::size_t num_bytes = in_mem_size + wei_mem_size + out_mem_size;
+
+    using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD<NumDimSpatial,
+                                                                                   InLayout,
+                                                                                   WeiLayout,
+                                                                                   ck::Tuple<>,
+                                                                                   OutLayout,
+                                                                                   InDataType,
+                                                                                   WeiDataType,
+                                                                                   ck::Tuple<>,
+                                                                                   OutDataType,
+                                                                                   PassThrough,
+                                                                                   PassThrough,
+                                                                                   PassThrough,
+                                                                                   AComputeType,
+                                                                                   BComputeType>;
+    // get device op instances
+    const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
+        DeviceOp>::GetInstances();
+
+    std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
+
+    std::string best_op_name;
+    int best_op_id        = -1;
+    float best_avg_time   = std::numeric_limits<float>::max();
+    float best_gb_per_sec = 0;
+    float best_tflops     = 0;
+
+    // profile device operation instances
+    std::cout << "Run all instances and do timing" << std::endl;
+
+    for(int i = 0; i < op_ptrs.size(); ++i)
+    {
+        auto& op_ptr      = op_ptrs[i];
+        auto argument_ptr = op_ptr->MakeArgumentPointer(
+            in.GetDeviceBuffer(),
+            wei.GetDeviceBuffer(),
+            std::array<const void*, 0>{},
+            out.GetDeviceBuffer(),
+            in_lengths,
+            in_strides,
+            wei_lengths,
+            wei_strides,
+            std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 0>{{}},
+            std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 0>{{}},
+            out_lengths,
+            out_strides,
+            conv_filter_strides,
+            conv_filter_dilations,
+            input_left_pads,
+            input_right_pads,
+            PassThrough{},
+            PassThrough{},
+            PassThrough{});
+
+        auto invoker_ptr    = op_ptr->MakeInvokerPointer();
+        std::string op_name = op_ptr->GetTypeString();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
+
+            float tflops     = static_cast<float>(flop) / 1.E9 / avg_time;
+            float gb_per_sec = num_bytes / 1.E6 / avg_time;
+
+            std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops << " TFlops, "
+                      << gb_per_sec << " GB/s, " << op_name << std::endl;
+
+            if(tflops > best_tflops)
+            {
+                best_op_id      = i;
+                best_op_name    = op_name;
+                best_avg_time   = avg_time;
+                best_gb_per_sec = gb_per_sec;
+                best_tflops     = tflops;
+            }
+        }
+        else
+        {
+            std::cerr << op_name << " does not support this problem" << std::endl;
+        }
+    }
+
+    if(best_op_id < 0)
+    {
+        std::cerr << "no suitable instance" << std::endl;
+        return false;
+    }
+
+    std::cout << "Best Perf: " << std::setw(10) << best_avg_time << " ms, " << best_tflops
+              << " TFlops, " << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
+
+    // run the best intance
+    {
+        auto& op_ptr = op_ptrs[best_op_id];
+        std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
+                  << std::endl;
+        auto argument_ptr = op_ptr->MakeArgumentPointer(
+            in.GetDeviceBuffer(),
+            wei.GetDeviceBuffer(),
+            std::array<const void*, 0>{},
+            out.GetDeviceBuffer(),
+            in_lengths,
+            in_strides,
+            wei_lengths,
+            wei_strides,
+            std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 0>{{}},
+            std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 0>{{}},
+            out_lengths,
+            out_strides,
+            conv_filter_strides,
+            conv_filter_dilations,
+            input_left_pads,
+            input_right_pads,
+            PassThrough{},
+            PassThrough{},
+            PassThrough{});
+
+        auto invoker_ptr = op_ptr->MakeInvokerPointer();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
+        }
+
+        std::cout << "Done" << std::endl;
+    }
+    return true;
+}

client_example/07_grouped_convnd_fwd/grouped_conv1d_fwd.cpp

 // SPDX-License-Identifier: MIT
 // Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
 
-#include <cstdlib>
-#include <iomanip>
-#include <iostream>
-#include <iterator>
-#include <numeric>
-#include <vector>
+#include "common.hpp"
 
 #include "ck/ck.hpp"
-#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward.hpp"
 #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
-#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
 
 using InDataType  = ck::half_t;
 using WeiDataType = ck::half_t;
@@ -31,199 +24,16 @@ static constexpr ck::index_t X             = 3;
 static constexpr ck::index_t Wi            = 28;
 static constexpr ck::index_t Wo            = 28;
 
-struct SimpleDeviceMem
-{
-    SimpleDeviceMem() = delete;
-
-    SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
-    {
-        (void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
-    }
-
-    void* GetDeviceBuffer() { return p_mem_; }
-
-    ~SimpleDeviceMem() { (void)hipFree(p_mem_); }
-
-    void* p_mem_;
-};
-
 int main()
 {
-    std::array<ck::index_t, NumDimSpatial + 3> in_lengths{G, N, Wi, C};
-    std::array<ck::index_t, NumDimSpatial + 3> in_strides{0, 0, 0, 1};
-
-    std::array<ck::index_t, NumDimSpatial + 3> wei_lengths{G, K, X, C};
-    std::array<ck::index_t, NumDimSpatial + 3> wei_strides{0, 0, 0, 1};
-
-    std::array<ck::index_t, NumDimSpatial + 3> out_lengths{G, N, Wo, K};
-    std::array<ck::index_t, NumDimSpatial + 3> out_strides{0, 0, 0, 1};
-
-    std::partial_sum(rbegin(in_lengths),
-                     std::prev(rend(in_lengths)),
-                     std::next(rbegin(in_strides)),
-                     std::multiplies<>{});
-    std::partial_sum(rbegin(wei_lengths),
-                     std::prev(rend(wei_lengths)),
-                     std::next(rbegin(wei_strides)),
-                     std::multiplies<>{});
-    std::partial_sum(rbegin(out_lengths),
-                     std::prev(rend(out_lengths)),
-                     std::next(rbegin(out_strides)),
-                     std::multiplies<>{});
-
-    // transpose GNWC/GKXC/GNWK to GNCW/GKCX/GNCW
-    std::rotate(rbegin(in_lengths),
-                std::next(rbegin(in_lengths)),
-                std::next(rbegin(in_lengths), NumDimSpatial + 1));
-    std::rotate(rbegin(in_strides),
-                std::next(rbegin(in_strides)),
-                std::next(rbegin(in_strides), NumDimSpatial + 1));
-    std::rotate(rbegin(wei_lengths),
-                std::next(rbegin(wei_lengths)),
-                std::next(rbegin(wei_lengths), NumDimSpatial + 1));
-    std::rotate(rbegin(wei_strides),
-                std::next(rbegin(wei_strides)),
-                std::next(rbegin(wei_strides), NumDimSpatial + 1));
-    std::rotate(rbegin(out_lengths),
-                std::next(rbegin(out_lengths)),
-                std::next(rbegin(out_lengths), NumDimSpatial + 1));
-    std::rotate(rbegin(out_strides),
-                std::next(rbegin(out_strides)),
-                std::next(rbegin(out_strides), NumDimSpatial + 1));
-
-    std::array<ck::index_t, NumDimSpatial> filter_strides{1};
-    std::array<ck::index_t, NumDimSpatial> filter_dilations{1};
-    std::array<ck::index_t, NumDimSpatial> input_left_pads{1};
-    std::array<ck::index_t, NumDimSpatial> input_right_pads{1};
-
-    SimpleDeviceMem in(sizeof(InDataType) * G * N * Wi * C);
-    SimpleDeviceMem wei(sizeof(WeiDataType) * G * K * X * C);
-    SimpleDeviceMem out(sizeof(OutDataType) * G * N * Wo * K);
-
-    using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD<NumDimSpatial,
-                                                                                   InLayout,
-                                                                                   WeiLayout,
-                                                                                   ck::Tuple<>,
-                                                                                   OutLayout,
-                                                                                   InDataType,
-                                                                                   WeiDataType,
-                                                                                   ck::Tuple<>,
-                                                                                   OutDataType,
-                                                                                   PassThrough,
-                                                                                   PassThrough,
-                                                                                   PassThrough>;
-
-    // get device op instances
-    const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
-        DeviceOp>::GetInstances();
-
-    std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
-
-    std::string best_op_name;
-    int best_op_id        = -1;
-    float best_avg_time   = std::numeric_limits<float>::max();
-    float best_gb_per_sec = 0;
-    float best_tflops     = 0;
-
-    // profile device operation instances
-    std::cout << "Run all instances and do timing" << std::endl;
-
-    for(int i = 0; i < op_ptrs.size(); ++i)
-    {
-        auto& op_ptr        = op_ptrs[i];
-        auto argument_ptr   = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
-                                                        wei.GetDeviceBuffer(),
-                                                        {},
-                                                        out.GetDeviceBuffer(),
-                                                        in_lengths,
-                                                        in_strides,
-                                                        wei_lengths,
-                                                        wei_strides,
-                                                        {},
-                                                        {},
-                                                        out_lengths,
-                                                        out_strides,
-                                                        filter_strides,
-                                                        filter_dilations,
-                                                        input_left_pads,
-                                                        input_right_pads,
-                                                        PassThrough{},
-                                                        PassThrough{},
-                                                        PassThrough{});
-        auto invoker_ptr    = op_ptr->MakeInvokerPointer();
-        std::string op_name = op_ptr->GetTypeString();
-
-        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
-        {
-            float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
-
-            std::size_t flop      = std::size_t(2) * G * N * K * C * Wo * X;
-            std::size_t num_bytes = sizeof(InDataType) * G * N * Wi * C +
-                                    sizeof(WeiDataType) * G * K * X * C +
-                                    sizeof(OutDataType) * G * N * Wo * K;
-
-            float tflops     = static_cast<float>(flop) / 1.E9 / avg_time;
-            float gb_per_sec = num_bytes / 1.E6 / avg_time;
-
-            std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops << " TFlops, "
-                      << gb_per_sec << " GB/s, " << op_name << std::endl;
-
-            if(tflops > best_tflops)
-            {
-                best_op_id      = i;
-                best_op_name    = op_name;
-                best_avg_time   = avg_time;
-                best_gb_per_sec = gb_per_sec;
-                best_tflops     = tflops;
-            }
-        }
-        else
-        {
-            std::cerr << op_name << " does not support this problem" << std::endl;
-        }
-    }
-
-    if(best_op_id < 0)
-    {
-        std::cerr << "no suitable instance" << std::endl;
-        return EXIT_FAILURE;
-    }
-
-    std::cout << "Best Perf: " << std::setw(10) << best_avg_time << " ms, " << best_tflops
-              << " TFlops, " << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
-
-    // run the best intance
-    {
-        auto& op_ptr = op_ptrs[best_op_id];
-        std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
-                  << std::endl;
-        auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
-                                                        wei.GetDeviceBuffer(),
-                                                        {},
-                                                        out.GetDeviceBuffer(),
-                                                        in_lengths,
-                                                        in_strides,
-                                                        wei_lengths,
-                                                        wei_strides,
-                                                        {},
-                                                        {},
-                                                        out_lengths,
-                                                        out_strides,
-                                                        filter_strides,
-                                                        filter_dilations,
-                                                        input_left_pads,
-                                                        input_right_pads,
-                                                        PassThrough{},
-                                                        PassThrough{},
-                                                        PassThrough{});
-
-        auto invoker_ptr = op_ptr->MakeInvokerPointer();
-
-        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
-        {
-            invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
-        }
-
-        std::cout << "Done" << std::endl;
-    }
+    return run_grouped_conv_fwd<NumDimSpatial,
+                                InDataType,
+                                WeiDataType,
+                                OutDataType,
+                                InLayout,
+                                WeiLayout,
+                                OutLayout,
+                                3>({N, Wi, G, C}, {G, K, X, C}, {N, Wo, G, K})
+               ? EXIT_SUCCESS
+               : EXIT_FAILURE;
 }

client_example/07_grouped_convnd_fwd/grouped_conv2d_fwd.cpp

 // SPDX-License-Identifier: MIT
 // Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
 
-#include <cstdlib>
-#include <iomanip>
-#include <iostream>
-#include <iterator>
-#include <numeric>
-#include <vector>
+#include "common.hpp"
 
 #include "ck/ck.hpp"
-#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward.hpp"
 #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
-#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
 
 using InDataType  = ck::half_t;
 using WeiDataType = ck::half_t;
@@ -34,167 +27,16 @@ static constexpr ck::index_t Wi            = 28;  // input W
 static constexpr ck::index_t Ho            = 28;  // output H
 static constexpr ck::index_t Wo            = 28;  // output W
 
-struct SimpleDeviceMem
-{
-    SimpleDeviceMem() = delete;
-
-    SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
-    {
-        (void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
-    }
-
-    void* GetDeviceBuffer() { return p_mem_; }
-
-    ~SimpleDeviceMem() { (void)hipFree(p_mem_); }
-
-    void* p_mem_;
-};
-
 int main()
 {
-    // We have NHWGC/GKYXC/NHWGK (x, weight, y) in memory space
-    // However, CK's API only accept length and stride with order of GNCHW/GKCYX/GNCHW
-    // Hence, we need to adjust the order of stride
-    std::array<ck::index_t, 5> in_lengths{G, N, C, Hi, Wi};
-    std::array<ck::index_t, 5> in_strides{C, Hi * Wi * G * C, 1, Wi * G * C, G * C};
-    std::array<ck::index_t, 5> wei_lengths{G, K, C, Y, X};
-    std::array<ck::index_t, 5> wei_strides{K * Y * X * C, Y * X * C, 1, X * C, C};
-    std::array<ck::index_t, 5> out_lengths{G, N, K, Ho, Wo};
-    std::array<ck::index_t, 5> out_strides{C, Ho * Wo * G * C, 1, Wo * G * C, G * C};
-
-    std::array<ck::index_t, NumDimSpatial> filter_strides{1, 1};
-    std::array<ck::index_t, NumDimSpatial> filter_dilations{1, 1};
-    std::array<ck::index_t, NumDimSpatial> input_left_pads{1, 1};
-    std::array<ck::index_t, NumDimSpatial> input_right_pads{1, 1};
-
-    SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * G * C);
-    SimpleDeviceMem wei(sizeof(WeiDataType) * G * K * Y * X * C);
-    SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * G * K);
-
-    using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD<NumDimSpatial,
-                                                                                   InLayout,
-                                                                                   WeiLayout,
-                                                                                   ck::Tuple<>,
-                                                                                   OutLayout,
-                                                                                   InDataType,
-                                                                                   WeiDataType,
-                                                                                   ck::Tuple<>,
-                                                                                   OutDataType,
-                                                                                   PassThrough,
-                                                                                   PassThrough,
-                                                                                   PassThrough>;
-
-    // get device op instances
-    const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
-        DeviceOp>::GetInstances();
-
-    std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
-
-    std::string best_op_name;
-    int best_op_id        = -1;
-    float best_avg_time   = std::numeric_limits<float>::max();
-    float best_gb_per_sec = 0;
-    float best_tflops     = 0;
-
-    // profile device operation instances
-    std::cout << "Run all instances and do timing" << std::endl;
-
-    for(int i = 0; i < op_ptrs.size(); ++i)
-    {
-        auto& op_ptr        = op_ptrs[i];
-        auto argument_ptr   = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
-                                                        wei.GetDeviceBuffer(),
-                                                        {},
-                                                        out.GetDeviceBuffer(),
-                                                        in_lengths,
-                                                        in_strides,
-                                                        wei_lengths,
-                                                        wei_strides,
-                                                        {},
-                                                        {},
-                                                        out_lengths,
-                                                        out_strides,
-                                                        filter_strides,
-                                                        filter_dilations,
-                                                        input_left_pads,
-                                                        input_right_pads,
-                                                        PassThrough{},
-                                                        PassThrough{},
-                                                        PassThrough{});
-        auto invoker_ptr    = op_ptr->MakeInvokerPointer();
-        std::string op_name = op_ptr->GetTypeString();
-
-        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
-        {
-            float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
-
-            std::size_t flop      = std::size_t(2) * G * N * K * C * Ho * Wo * Y * X;
-            std::size_t num_bytes = sizeof(InDataType) * N * Hi * Wi * G * C +
-                                    sizeof(WeiDataType) * G * K * Y * X * C +
-                                    sizeof(OutDataType) * N * Ho * Wo * G * K;
-
-            float tflops     = static_cast<float>(flop) / 1.E9 / avg_time;
-            float gb_per_sec = num_bytes / 1.E6 / avg_time;
-
-            std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops << " TFlops, "
-                      << gb_per_sec << " GB/s, " << op_name << std::endl;
-
-            if(tflops > best_tflops)
-            {
-                best_op_id      = i;
-                best_op_name    = op_name;
-                best_avg_time   = avg_time;
-                best_gb_per_sec = gb_per_sec;
-                best_tflops     = tflops;
-            }
-        }
-        else
-        {
-            std::cerr << op_name << " does not support this problem" << std::endl;
-        }
-    }
-
-    if(best_op_id < 0)
-    {
-        std::cerr << "no suitable instance" << std::endl;
-        return EXIT_FAILURE;
-    }
-
-    std::cout << "Best Perf: " << std::setw(10) << best_avg_time << " ms, " << best_tflops
-              << " TFlops, " << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
-
-    // run the best intance
-    {
-        auto& op_ptr = op_ptrs[best_op_id];
-        std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
-                  << std::endl;
-        auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
-                                                        wei.GetDeviceBuffer(),
-                                                        {},
-                                                        out.GetDeviceBuffer(),
-                                                        in_lengths,
-                                                        in_strides,
-                                                        wei_lengths,
-                                                        wei_strides,
-                                                        {},
-                                                        {},
-                                                        out_lengths,
-                                                        out_strides,
-                                                        filter_strides,
-                                                        filter_dilations,
-                                                        input_left_pads,
-                                                        input_right_pads,
-                                                        PassThrough{},
-                                                        PassThrough{},
-                                                        PassThrough{});
-
-        auto invoker_ptr = op_ptr->MakeInvokerPointer();
-
-        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
-        {
-            invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
-        }
-
-        std::cout << "Done" << std::endl;
-    }
+    return run_grouped_conv_fwd<NumDimSpatial,
+                                InDataType,
+                                WeiDataType,
+                                OutDataType,
+                                InLayout,
+                                WeiLayout,
+                                OutLayout,
+                                3>({N, Hi, Wi, G, C}, {G, K, Y, X, C}, {N, Ho, Wo, G, K})
+               ? EXIT_SUCCESS
+               : EXIT_FAILURE;
 }

client_example/16_convnd_fwd/CMakeLists.txt

@@ -7,22 +7,6 @@ endif()
 if((DTYPES MATCHES "fp8") OR NOT DEFINED DTYPES)
     add_executable(client_conv3d_fwd_fp16_comp_fp8 conv3d_fwd_fp16_comp_fp8.cpp)
     target_link_libraries(client_conv3d_fwd_fp16_comp_fp8 PRIVATE composable_kernel::device_conv_operations)
-
-    add_executable(client_conv3d_fwd_fp8 conv3d_fwd_fp8.cpp)
-    target_link_libraries(client_conv3d_fwd_fp8 PRIVATE composable_kernel::device_conv_operations)
-endif()
-
-if((DTYPES MATCHES "bf8") OR NOT DEFINED DTYPES)
-    add_executable(client_conv3d_fwd_bf8 conv3d_fwd_bf8.cpp)
-    target_link_libraries(client_conv3d_fwd_bf8 PRIVATE composable_kernel::device_conv_operations)
-endif()
-
-if((DTYPES MATCHES "fp8" AND DTYPES MATCHES "bf8") OR NOT DEFINED DTYPES)
-    add_executable(client_conv3d_fwd_fp8_bf8 conv3d_fwd_fp8_bf8.cpp)
-    target_link_libraries(client_conv3d_fwd_fp8_bf8 PRIVATE composable_kernel::device_conv_operations)
-
-    add_executable(client_conv3d_fwd_bf8_fp8 conv3d_fwd_bf8_fp8.cpp)
-    target_link_libraries(client_conv3d_fwd_bf8_fp8 PRIVATE composable_kernel::device_conv_operations)
 endif()
 
 if((DTYPES MATCHES "fp32") OR NOT DEFINED DTYPES)

example/CMakeLists.txt

@@ -44,6 +44,13 @@ function(add_example_executable EXAMPLE_NAME FILE_NAME)
         endif()
     endforeach()
     endif()
+
+    if(INSTANCES_ONLY)
+        set(EX_TARGETS ${DEFAULT_GPU_TARGETS})
+    else()
+        set(EX_TARGETS ${GPU_TARGETS})
+    endif()
+
     #Do not build any DL examples if DL_KERNELS not set
     foreach(source IN LISTS FILE_NAME)
         if(NOT DEFINED DL_KERNELS AND source MATCHES "_dl")
@@ -53,23 +60,30 @@ function(add_example_executable EXAMPLE_NAME FILE_NAME)
     endforeach()
     #Do not build any XDL examples if gfx9 targets are not on the list
     foreach(source IN LISTS FILE_NAME)
-        if(NOT GPU_TARGETS MATCHES "gfx9" AND source MATCHES "_xdl")
+        if(NOT EX_TARGETS MATCHES "gfx9" AND source MATCHES "_xdl")
             message("removing xdl example ${source} ")
             list(REMOVE_ITEM FILE_NAME "${source}")
         endif()
     endforeach()
     #Do not build any WMMA examples if gfx11 targets are not on the list
     foreach(source IN LISTS FILE_NAME)
-        if(NOT GPU_TARGETS MATCHES "gfx11" AND source MATCHES "_wmma")
+        if(NOT EX_TARGETS MATCHES "gfx11" AND source MATCHES "_wmma")
             message("removing wmma example ${source} ")
             list(REMOVE_ITEM FILE_NAME "${source}")
         endif()
     endforeach()
     #only continue if there are some source files left on the list
     if(FILE_NAME)
+        if(FILE_NAME MATCHES "_xdl")
+            list(REMOVE_ITEM EX_TARGETS gfx1030 gfx1100 gfx1101 gfx1102 gfx1103)
+        elseif(FILE_NAME MATCHES "_wmma")
+            list(REMOVE_ITEM EX_TARGETS gfx908 gfx90a gfx940 gfx941 gfx942 gfx1030)
+        endif()
+        set_source_files_properties(${FILE_NAME} PROPERTIES LANGUAGE HIP)
         add_executable(${EXAMPLE_NAME} ${FILE_NAME})
         target_link_libraries(${EXAMPLE_NAME} PRIVATE utility)
         add_test(NAME ${EXAMPLE_NAME} COMMAND $<TARGET_FILE:${EXAMPLE_NAME}> ${ARGN})
+        set_property(TARGET ${EXAMPLE_NAME} PROPERTY HIP_ARCHITECTURES ${EX_TARGETS} )
         add_dependencies(examples ${EXAMPLE_NAME})
         add_dependencies(check ${EXAMPLE_NAME})
         rocm_install(TARGETS ${EXAMPLE_NAME} COMPONENT examples)
@@ -118,6 +132,12 @@ function(add_example_executable_no_testing EXAMPLE_NAME FILE_NAME)
         endif()
     endforeach()
     endif()
+
+    if(INSTANCES_ONLY)
+        set(EX_TARGETS ${DEFAULT_GPU_TARGETS})
+    else()
+        set(EX_TARGETS ${GPU_TARGETS})
+    endif()
     #Do not build any DL examples if DL_KERNELS not set
     foreach(source IN LISTS FILE_NAME)
         if(NOT DEFINED DL_KERNELS AND source MATCHES "_dl")
@@ -127,23 +147,30 @@ function(add_example_executable_no_testing EXAMPLE_NAME FILE_NAME)
     endforeach()
     #Do not build any XDL examples if gfx9 targets are not on the list
     foreach(source IN LISTS FILE_NAME)
-        if(NOT GPU_TARGETS MATCHES "gfx9" AND source MATCHES "_xdl")
+        if(NOT EX_TARGETS MATCHES "gfx9" AND source MATCHES "_xdl")
             message("removing xdl example ${source} ")
             list(REMOVE_ITEM FILE_NAME "${source}")
         endif()
     endforeach()
     #Do not build any WMMA examples if gfx11 targets are not on the list
     foreach(source IN LISTS FILE_NAME)
-        if(NOT GPU_TARGETS MATCHES "gfx11" AND source MATCHES "_wmma")
+        if(NOT EX_TARGETS MATCHES "gfx11" AND source MATCHES "_wmma")
             message("removing wmma example ${source} ")
             list(REMOVE_ITEM FILE_NAME "${source}")
         endif()
     endforeach()
     #only continue if there are some source files left on the list
     if(FILE_NAME)
+        if(FILE_NAME MATCHES "_xdl")
+            list(REMOVE_ITEM EX_TARGETS gfx900 gfx906 gfx1030 gfx1100 gfx1101 gfx1102 gfx1103)
+        elseif(FILE_NAME MATCHES "_wmma")
+            list(REMOVE_ITEM EX_TARGETS gfx900 gfx906 gfx908 gfx90a gfx940 gfx941 gfx942 gfx1030)
+        endif()
+        set_source_files_properties(${FILE_NAME} PROPERTIES LANGUAGE HIP)
         add_executable(${EXAMPLE_NAME} ${FILE_NAME})
         target_link_libraries(${EXAMPLE_NAME} PRIVATE utility)
         add_dependencies(examples ${EXAMPLE_NAME})
+        set_property(TARGET ${EXAMPLE_NAME} PROPERTY HIP_ARCHITECTURES ${EX_TARGETS} )
         rocm_install(TARGETS ${EXAMPLE_NAME} COMPONENT examples)
         set(result 0)
     endif()

include/ck/host_utility/flush_cache.hpp

@@ -104,14 +104,19 @@ inline void flush_icache()
     hip_check_error(hipGetLastError());
 }
 // if TimePrePress == false, return time does not include preprocess's time
-template <bool TimePreprocess, typename Args, typename F, typename PreProcessFunc>
+template <bool TimePreprocess,
+          typename GemmArgs,
+          typename... Args,
+          typename F,
+          typename PreProcessFunc>
 float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
                                              PreProcessFunc preprocess,
                                              F kernel,
                                              dim3 grid_dim,
                                              dim3 block_dim,
                                              std::size_t lds_byte,
-                                             Args& args)
+                                             GemmArgs& gemm_args,
+                                             Args... args)
 {
 #if CK_TIME_KERNEL
 #define MEDIAN 1
@@ -133,7 +138,7 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
         // warm up
         for(int i = 0; i < stream_config.cold_niters_; ++i)
         {
-            kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(args);
+            kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(gemm_args, args...);
             hip_check_error(hipGetLastError());
         }
 
@@ -172,7 +177,7 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
                 preprocess();
             }
             // run real kernel
-            kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(args);
+            kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(gemm_args, args...);
             hip_check_error(hipGetLastError());
             // end real kernel
 
@@ -190,9 +195,9 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
             {
                 std::cout << "i: " << i << " cur_time: " << cur_time << std::endl;
 
-                printf("args.p_a_grid: %p, args.p_b_grid:%p\n",
-                       static_cast<const void*>(args.p_a_grid),
-                       static_cast<const void*>(args.p_b_grid));
+                printf("gemm_args.p_a_grid: %p, gemm_args.p_b_grid:%p\n",
+                       static_cast<const void*>(gemm_args.p_a_grid),
+                       static_cast<const void*>(gemm_args.p_b_grid));
             }
         }
 
@@ -216,13 +221,13 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
     else
     {
         preprocess();
-        kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(args);
+        kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(gemm_args, args...);
         hip_check_error(hipGetLastError());
 
         return 0;
     }
 #else
-    kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(args);
+    kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(gemm_args, args...);
     hip_check_error(hipGetLastError());
 
     return 0;

include/ck/tensor_description/multi_index_transform.hpp

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
 
@@ -1952,7 +1952,7 @@ struct Modulo
     }
 };
 
-template <typename LowLengths>
+template <typename LowLengths, bool ApplyModulo>
 struct Xor
 {
     using LowerIndex = MultiIndex<2>;
@@ -1981,8 +1981,15 @@ struct Xor
 
         idx_low(Number<0>{}) = idx_up[Number<0>{}];
 
-        idx_low(Number<1>{}) =
-            idx_up[Number<1>{}] ^ (idx_up[Number<0>{}] % up_lengths_[Number<1>{}]);
+        if constexpr(ApplyModulo)
+        {
+            idx_low(Number<1>{}) =
+                idx_up[Number<1>{}] ^ (idx_up[Number<0>{}] % up_lengths_[Number<1>{}]);
+        }
+        else
+        {
+            idx_low(Number<1>{}) = idx_up[Number<1>{}] ^ idx_up[Number<0>{}];
+        }
     }
 
     template <typename LowIdxDiff,

include/ck/tensor_description/multi_index_transform_helper.hpp

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
 
@@ -128,9 +128,15 @@ __host__ __device__ constexpr auto make_modulo_transform(const Modulus& modulus,
     return Modulo<Modulus, UpLength>{modulus, up_length};
 }
 
+template <typename LowLengths>
+__host__ __device__ constexpr auto make_xor_with_modulo_transform(const LowLengths& low_lengths)
+{
+    return Xor<LowLengths, true /*ApplyModulo*/>{low_lengths};
+}
+
 template <typename LowLengths>
 __host__ __device__ constexpr auto make_xor_transform(const LowLengths& low_lengths)
 {
-    return Xor<LowLengths>{low_lengths};
+    return Xor<LowLengths, false /*ApplyModulo*/>{low_lengths};
 }
 } // namespace ck

include/ck/tensor_operation/gpu/device/impl/device_contraction_multiple_d_xdl_cshuffle.hpp

@@ -53,8 +53,7 @@ __global__ void
                 e_grid_desc_mblock_mperblock_nblock_nperblock,
             const Block2ETileMap block_2_etile_map)
 {
-#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
-    defined(__gfx94__))
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx9__))
     __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
 
     GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid,

include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_two_stage_xdl_cshuffle.hpp

@@ -14,95 +14,137 @@
 #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
 #include "ck/tensor_operation/gpu/device/device_grouped_conv_bwd_weight.hpp"
 #include "ck/tensor_operation/operator_transform/transform_conv_bwd_weight_to_gemm.hpp"
+#include "ck/tensor_operation/operator_transform/transform_conv_bwd_weight_to_gemm_v2.hpp"
 #include "ck/tensor_operation/gpu/device/convolution_backward_weight_specialization.hpp"
 #include "ck/tensor_operation/gpu/grid/gridwise_elementwise_2d.hpp"
-#include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_bwd_weight.hpp"
+#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdl_cshuffle_bwd_weight_v3.hpp"
 #include <ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp>
 #include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_utils.hpp"
 #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
 
 #include "ck/host_utility/device_prop.hpp"
 #include "ck/host_utility/kernel_launch.hpp"
+#include "ck/host_utility/flush_cache.hpp"
 
 namespace ck {
 namespace tensor_operation {
 namespace device {
 
 template <typename GridwiseGemm,
-          typename FloatA,
-          typename FloatB,
-          typename FloatC,
-          typename AElementwiseOperation,
-          typename BElementwiseOperation,
-          typename CElementwiseOperation,
-          typename AGridDesc_B_K0_M_K1,
-          typename BGridDesc_B_K0_N_K1,
+          typename AGridDesc_AK0_M_K1,
+          typename BGridDesc_BK0_N_K1,
           typename CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
-          typename Block2CTileMap,
           typename ComputePtrOffsetOfBatch,
-          bool HasMainKBlockLoop>
+          index_t NumBatchToMerge,
+          bool HasMainKBlockLoop,
+          InMemoryDataOperationEnum CGlobalMemoryDataOperation,
+          index_t MinimumOccupancy = 1,
+          TailNumber TailNum       = TailNumber::Full>
 __global__ void
 #if CK_USE_LAUNCH_BOUNDS
-    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
+    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, MinimumOccupancy)
 #endif
-        kernel_batched_gemm_xdlops_bwd_weight(
-            const FloatA* __restrict__ p_a_grid,
-            const FloatB* __restrict__ p_b_grid,
-            FloatC* __restrict__ p_c_grid,
-            const AElementwiseOperation a_element_op,
-            const BElementwiseOperation b_element_op,
-            const CElementwiseOperation c_element_op,
-            const index_t batch_count,
-            const AGridDesc_B_K0_M_K1 a_b_k0_m_k1_grid_desc,
-            const BGridDesc_B_K0_N_K1 b_b_k0_n_k1_grid_desc,
+        kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3(
+            typename GridwiseGemm::Argument karg,
+            const AGridDesc_AK0_M_K1 a_grid_desc_ak0_m_ak1,
+            const BGridDesc_BK0_N_K1 b_grid_desc_bk0_n_bk1,
             const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
                 c_grid_desc_mblock_mperblock_nblock_nperblock,
-            const Block2CTileMap block_2_ctile_map,
-            const ComputePtrOffsetOfBatch compute_ptr_offset_of_batch)
+            const ComputePtrOffsetOfBatch compute_ptr_offset_of_batch,
+            const index_t num_k_per_block)
 {
 #if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
     defined(__gfx94__))
-    const index_t num_blocks_per_batch =
-        __builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
-    const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
+    const index_t g_idx = __builtin_amdgcn_readfirstlane(blockIdx.z * NumBatchToMerge);
+    const index_t k_idx = __builtin_amdgcn_readfirstlane(blockIdx.y * num_k_per_block);
 
     const long_index_t a_batch_offset = __builtin_amdgcn_readfirstlane(
         static_cast<long_index_t>(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)));
     const long_index_t b_batch_offset = __builtin_amdgcn_readfirstlane(
         static_cast<long_index_t>(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx)));
-    const long_index_t c_batch_offset = __builtin_amdgcn_readfirstlane(
-        static_cast<long_index_t>(compute_ptr_offset_of_batch.GetCPtrOffset(g_idx)));
-
-    __shared__ FloatA p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte() / sizeof(FloatA)];
-
-    GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid + a_batch_offset,
-                                                  p_b_grid + b_batch_offset,
-                                                  p_c_grid + c_batch_offset,
-                                                  p_shared,
-                                                  a_b_k0_m_k1_grid_desc,
-                                                  b_b_k0_n_k1_grid_desc,
-                                                  c_grid_desc_mblock_mperblock_nblock_nperblock,
-                                                  a_element_op,
-                                                  b_element_op,
-                                                  c_element_op,
-                                                  block_2_ctile_map);
+    const long_index_t e_batch_offset = __builtin_amdgcn_readfirstlane(
+        static_cast<long_index_t>(compute_ptr_offset_of_batch.GetEPtrOffset(g_idx)));
+
+    __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
+
+    GridwiseGemm::template Run<AGridDesc_AK0_M_K1,
+                               BGridDesc_BK0_N_K1,
+                               CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
+                               HasMainKBlockLoop,
+                               CGlobalMemoryDataOperation,
+                               TailNum>(karg.p_a_grid + a_batch_offset,
+                                        karg.p_b_grid + b_batch_offset,
+                                        karg.p_c_grid + e_batch_offset,
+                                        p_shared,
+                                        karg,
+                                        a_grid_desc_ak0_m_ak1,
+                                        b_grid_desc_bk0_n_bk1,
+                                        c_grid_desc_mblock_mperblock_nblock_nperblock,
+                                        k_idx);
 #else
-    ignore = p_a_grid;
-    ignore = p_b_grid;
-    ignore = p_c_grid;
-    ignore = a_b_k0_m_k1_grid_desc;
-    ignore = b_b_k0_n_k1_grid_desc;
-    ignore = c_grid_desc_mblock_mperblock_nblock_nperblock;
-    ignore = a_element_op;
-    ignore = b_element_op;
-    ignore = c_element_op;
-    ignore = batch_count;
-    ignore = block_2_ctile_map;
-    ignore = compute_ptr_offset_of_batch;
-
-    compute_ptr_offset_of_batch.GetAPtrOffset(0);
-    compute_ptr_offset_of_batch.GetBPtrOffset(0);
-    compute_ptr_offset_of_batch.GetCPtrOffset(0);
+    ignore = karg;
+#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
+}
+
+template <typename GridwiseGemm,
+          typename AGridDesc_AK0_M_K1,
+          typename BGridDesc_BK0_N_K1,
+          typename CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
+          typename ComputePtrOffsetOfBatch,
+          index_t NumBatchToMerge,
+          bool HasMainKBlockLoop,
+          InMemoryDataOperationEnum CGlobalMemoryDataOperation,
+          index_t MinimumOccupancy = 1,
+          TailNumber TailNum       = TailNumber::Full>
+__global__ void
+#if CK_USE_LAUNCH_BOUNDS
+    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, MinimumOccupancy)
+#endif
+        kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3_2lds(
+            typename GridwiseGemm::Argument karg,
+            const AGridDesc_AK0_M_K1 a_grid_desc_ak0_m_ak1,
+            const BGridDesc_BK0_N_K1 b_grid_desc_bk0_n_bk1,
+            const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
+                c_grid_desc_mblock_mperblock_nblock_nperblock,
+            const ComputePtrOffsetOfBatch compute_ptr_offset_of_batch,
+            const index_t num_k_per_block)
+{
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
+    defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__))
+    // offset base pointer for each work-group
+    const index_t g_idx = __builtin_amdgcn_readfirstlane(blockIdx.z * NumBatchToMerge);
+    const index_t k_idx = __builtin_amdgcn_readfirstlane(blockIdx.y * num_k_per_block);
+
+    const long_index_t a_batch_offset = __builtin_amdgcn_readfirstlane(
+        static_cast<long_index_t>(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)));
+    const long_index_t b_batch_offset = __builtin_amdgcn_readfirstlane(
+        static_cast<long_index_t>(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx)));
+    const long_index_t e_batch_offset = __builtin_amdgcn_readfirstlane(
+        static_cast<long_index_t>(compute_ptr_offset_of_batch.GetEPtrOffset(g_idx)));
+
+    // Pass two lds pointer is the key to tell compiler that ds_read/write
+    // operate on different lds chunk at same time without order dependecy
+    __shared__ char p_shared_0[GridwiseGemm::GetSharedMemoryNumberOfByte()];
+    __shared__ char p_shared_1[GridwiseGemm::GetSharedMemoryNumberOfByte()];
+
+    GridwiseGemm::template Run_2Lds<AGridDesc_AK0_M_K1,
+                                    BGridDesc_BK0_N_K1,
+                                    CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
+                                    HasMainKBlockLoop,
+                                    CGlobalMemoryDataOperation,
+                                    TailNum>(karg.p_a_grid + a_batch_offset,
+                                             karg.p_b_grid + b_batch_offset,
+                                             karg.p_c_grid + e_batch_offset,
+                                             p_shared_0,
+                                             p_shared_1,
+                                             karg,
+                                             a_grid_desc_ak0_m_ak1,
+                                             b_grid_desc_bk0_n_bk1,
+                                             c_grid_desc_mblock_mperblock_nblock_nperblock,
+                                             k_idx);
+#else
+    ignore = karg;
 #endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
 }
 
@@ -121,7 +163,7 @@ template <ck::index_t NDimSpatial,
           ck::index_t BlockSize,
           ck::index_t MPerBlock,
           ck::index_t NPerBlock,
-          ck::index_t K0PerBlock,
+          ck::index_t KPerBlock,
           ck::index_t K1,
           ck::index_t MPerXdl,
           ck::index_t NPerXdl,
@@ -145,8 +187,11 @@ template <ck::index_t NDimSpatial,
           index_t CShuffleNXdlPerWavePerShuffle,
           typename CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
           index_t CBlockTransferScalarPerVector_NWaveNPerXdl,
-          typename ComputeTypeA = InDataType,
-          typename ComputeTypeB = ComputeTypeA>
+          BlockGemmPipelineScheduler BlkGemmPipeSched = BlockGemmPipelineScheduler::Intrawave,
+          BlockGemmPipelineVersion BlkGemmPipelineVer = BlockGemmPipelineVersion::v1,
+          index_t NumBatchToMerge                     = 1,
+          typename ComputeTypeA                       = InDataType,
+          typename ComputeTypeB                       = ComputeTypeA>
 struct DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle
     : public DeviceGroupedConvBwdWeight<NDimSpatial,
                                         InLayout,
@@ -161,6 +206,10 @@ struct DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle
                                         ComputeTypeA,
                                         ComputeTypeB>
 {
+    static_assert(is_same_v<InElementwiseOperation, element_wise::PassThrough>);
+    static_assert(is_same_v<WeiElementwiseOperation, element_wise::PassThrough>);
+    static_assert(is_same_v<OutElementwiseOperation, element_wise::PassThrough>);
+
     using DeviceOp = DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle;
 
     using ADataType = OutDataType;
@@ -183,101 +232,123 @@ struct DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle
 
     static constexpr auto K1Number = Number<K1>{};
 
-    static constexpr auto conv_to_gemm_transformer =
+    static constexpr auto conv_to_gemm_transformer_v2 =
+        TransformConvBwdWeightToGemmV2<NDimSpatial,
+                                       MPerBlock,
+                                       NPerBlock,
+                                       K1Number,
+                                       KPerBlock / K1Number,
+                                       NumBatchToMerge,
+                                       ConvBackwardWeightSpecialization>{};
+
+    static constexpr auto conv_to_gemm_transformer_v1 =
         TransformConvBwdWeightToGemm<NDimSpatial,
                                      MPerBlock,
                                      NPerBlock,
                                      K1Number,
-                                     K0PerBlock,
+                                     KPerBlock / K1Number,
                                      ConvBackwardWeightSpecialization>{};
 
-    // Bytes per 32 lds bank: 32 * 4 bytes
-    static constexpr auto BankLength = 128;
-    static constexpr auto ElePerBank = BankLength / sizeof(ADataType);
-
-    // M1 & M0
-    static constexpr auto ABlockLdsM1PerBlock = ElePerBank / K1;
-    static constexpr auto ABlockLdsM0PerBlock = MPerBlock / ABlockLdsM1PerBlock;
-    static constexpr auto ABlockLdsM1Padding  = 4;
+    static constexpr GemmSpecialization GemmSpec = GemmSpecialization::Default;
 
-    // N1 & N0
-    static constexpr auto BBlockLdsN1PerBlock = ElePerBank / K1;
-    static constexpr auto BBlockLdsN0PerBlock = NPerBlock / BBlockLdsN1PerBlock;
-    static constexpr auto BBlockLdsN1Padding  = 4;
+    template <ck::index_t NDim, typename ck::enable_if<NDim == 2, bool>::type = false>
+    static auto GetABCGridDesc()
+    {
+        const ck::index_t dim   = 1;
+        const ck::index_t batch = 1;
+        const std::array<ck::index_t, NDimSpatial> lengths{1, 1};
+        const std::array<ck::index_t, NDimSpatial + 3> strides{1, 1, 1, 1, 1};
+        const std::array<ck::index_t, NDimSpatial> params{1, 1};
+        return conv_to_gemm_transformer_v2
+            .template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<2>(dim,
+                                                                         dim,
+                                                                         dim,
+                                                                         lengths,
+                                                                         lengths,
+                                                                         lengths,
+                                                                         strides,
+                                                                         strides,
+                                                                         strides,
+                                                                         params,
+                                                                         params,
+                                                                         params,
+                                                                         params,
+                                                                         batch);
+    }
 
-    template <ck::index_t NDim, typename ck::enable_if<NDim == 1, bool>::type = false>
+    template <ck::index_t NDim, typename ck::enable_if<NDim == 3, bool>::type = false>
     static auto GetABCGridDesc()
     {
         const ck::index_t dim   = 1;
         const ck::index_t batch = 1;
-        const std::array<ck::index_t, NDimSpatial> lengths{1};
-        const std::array<ck::index_t, NDimSpatial + 3> strides{1, 1, 1, 1};
-        const std::array<ck::index_t, NDimSpatial> params{1};
-        return conv_to_gemm_transformer.template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<1>(
-            dim,
-            dim,
-            dim,
-            lengths,
-            lengths,
-            lengths,
-            strides,
-            strides,
-            strides,
-            params,
-            params,
-            params,
-            params,
-            batch);
+        const std::array<ck::index_t, NDimSpatial> lengths{1, 1, 1};
+        const std::array<ck::index_t, NDimSpatial + 3> strides{1, 1, 1, 1, 1, 1};
+        const std::array<ck::index_t, NDimSpatial> params{1, 1, 1};
+        return conv_to_gemm_transformer_v2
+            .template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<3>(dim,
+                                                                         dim,
+                                                                         dim,
+                                                                         lengths,
+                                                                         lengths,
+                                                                         lengths,
+                                                                         strides,
+                                                                         strides,
+                                                                         strides,
+                                                                         params,
+                                                                         params,
+                                                                         params,
+                                                                         params,
+                                                                         batch);
     }
 
     template <ck::index_t NDim, typename ck::enable_if<NDim == 2, bool>::type = false>
-    static auto GetABCGridDesc()
+    static auto GetElementwiseCGridDesc()
     {
         const ck::index_t dim   = 1;
         const ck::index_t batch = 1;
         const std::array<ck::index_t, NDimSpatial> lengths{1, 1};
         const std::array<ck::index_t, NDimSpatial + 3> strides{1, 1, 1, 1, 1};
         const std::array<ck::index_t, NDimSpatial> params{1, 1};
-        return conv_to_gemm_transformer.template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<2>(
-            dim,
-            dim,
-            dim,
-            lengths,
-            lengths,
-            lengths,
-            strides,
-            strides,
-            strides,
-            params,
-            params,
-            params,
-            params,
-            batch);
+        return conv_to_gemm_transformer_v1
+            .template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<2>(dim,
+                                                                         dim,
+                                                                         dim,
+                                                                         lengths,
+                                                                         lengths,
+                                                                         lengths,
+                                                                         strides,
+                                                                         strides,
+                                                                         strides,
+                                                                         params,
+                                                                         params,
+                                                                         params,
+                                                                         params,
+                                                                         batch)[I2];
     }
 
     template <ck::index_t NDim, typename ck::enable_if<NDim == 3, bool>::type = false>
-    static auto GetABCGridDesc()
+    static auto GetElementwiseCGridDesc()
     {
         const ck::index_t dim   = 1;
         const ck::index_t batch = 1;
         const std::array<ck::index_t, NDimSpatial> lengths{1, 1, 1};
         const std::array<ck::index_t, NDimSpatial + 3> strides{1, 1, 1, 1, 1, 1};
         const std::array<ck::index_t, NDimSpatial> params{1, 1, 1};
-        return conv_to_gemm_transformer.template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<3>(
-            dim,
-            dim,
-            dim,
-            lengths,
-            lengths,
-            lengths,
-            strides,
-            strides,
-            strides,
-            params,
-            params,
-            params,
-            params,
-            batch);
+        return conv_to_gemm_transformer_v1
+            .template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<3>(dim,
+                                                                         dim,
+                                                                         dim,
+                                                                         lengths,
+                                                                         lengths,
+                                                                         lengths,
+                                                                         strides,
+                                                                         strides,
+                                                                         strides,
+                                                                         params,
+                                                                         params,
+                                                                         params,
+                                                                         params,
+                                                                         batch)[I2];
     }
 
     using ABCGridDescs = decltype(GetABCGridDesc<NDimSpatial>());
@@ -285,60 +356,56 @@ struct DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle
     using AGridDesc_K0_M_K1 = remove_cvref_t<decltype(ABCGridDescs{}[I0])>;
     using BGridDesc_K0_N_K1 = remove_cvref_t<decltype(ABCGridDescs{}[I1])>;
     using CGridDesc_M_N     = remove_cvref_t<decltype(ABCGridDescs{}[I2])>;
-
-    using GridwiseGemm = GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_bwd_weight<
-        BlockSize,
-        ADataType,
-        BDataType,
-        AccDataType,
-        AccDataType,
-        InMemoryDataOperationEnum::AtomicAdd,
-        AGridDesc_K0_M_K1,
-        BGridDesc_K0_N_K1,
-        CGridDesc_M_N,
-        AElementwiseOperation,
-        BElementwiseOperation,
-        element_wise::PassThrough,
-        MPerBlock,
-        NPerBlock,
-        K0PerBlock,
-        MPerXdl,
-        NPerXdl,
-        K1,
-        MXdlPerWave,
-        NXdlPerWave,
-        ABlockTransferThreadClusterLengths_K0_M_K1,
-        ABlockTransferThreadClusterArrangeOrder,
-        ABlockTransferSrcAccessOrder,
-        ABlockTransferSrcVectorDim,
-        ABlockTransferSrcScalarPerVector,
-        ABlockTransferDstScalarPerVector_K1,
-        false, // AThreadTransferSrcResetCoordinateAfterRun,
-        ABlockLdsAddExtraM,
-        ABlockLdsM1PerBlock,
-        ABlockLdsM0PerBlock,
-        ABlockLdsM1Padding,
-        BBlockTransferThreadClusterLengths_K0_N_K1,
-        BBlockTransferThreadClusterArrangeOrder,
-        BBlockTransferSrcAccessOrder,
-        BBlockTransferSrcVectorDim,
-        BBlockTransferSrcScalarPerVector,
-        BBlockTransferDstScalarPerVector_K1,
-        false, // BThreadTransferSrcResetCoordinateAfterRun,
-        BBlockLdsAddExtraN,
-        BBlockLdsN1PerBlock,
-        BBlockLdsN0PerBlock,
-        BBlockLdsN1Padding,
-        CShuffleMXdlPerWavePerShuffle,
-        CShuffleNXdlPerWavePerShuffle,
-        CBlockTransferScalarPerVector_NWaveNPerXdl,
-        CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
-        true,
-        true,
-        1,
-        PipelineVersion::v1,
-        ComputeTypeA,
-        ComputeTypeB>;
+    using CElementwiseGridDesc_M_N =
+        remove_cvref_t<decltype(GetElementwiseCGridDesc<NDimSpatial>())>;
+
+    using GridwiseGemm =
+        GridwiseGemm_xdl_cshuffle_v3<tensor_layout::gemm::RowMajor,
+                                     tensor_layout::gemm::ColumnMajor,
+                                     tensor_layout::gemm::RowMajor,
+                                     ADataType,
+                                     BDataType,
+                                     AccDataType,
+                                     AccDataType,
+                                     AccDataType,
+                                     AElementwiseOperation,
+                                     BElementwiseOperation,
+                                     CDEElementwiseOperation,
+                                     GemmSpec,
+                                     BlockSize,
+                                     MPerBlock,
+                                     NPerBlock,
+                                     KPerBlock,
+                                     K1,
+                                     K1,
+                                     MPerXdl,
+                                     NPerXdl,
+                                     MXdlPerWave,
+                                     NXdlPerWave,
+                                     ABlockTransferThreadClusterLengths_K0_M_K1,
+                                     ABlockTransferThreadClusterArrangeOrder,
+                                     ABlockTransferSrcAccessOrder,
+                                     ABlockTransferSrcVectorDim,
+                                     ABlockTransferSrcScalarPerVector,
+                                     ABlockTransferDstScalarPerVector_K1,
+                                     false,
+                                     ABlockLdsAddExtraM,
+                                     BBlockTransferThreadClusterLengths_K0_N_K1,
+                                     BBlockTransferThreadClusterArrangeOrder,
+                                     BBlockTransferSrcAccessOrder,
+                                     BBlockTransferSrcVectorDim,
+                                     BBlockTransferSrcScalarPerVector,
+                                     BBlockTransferDstScalarPerVector_K1,
+                                     false,
+                                     BBlockLdsAddExtraN,
+                                     CShuffleMXdlPerWavePerShuffle,
+                                     CShuffleNXdlPerWavePerShuffle,
+                                     CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+                                     CBlockTransferScalarPerVector_NWaveNPerXdl,
+                                     BlkGemmPipeSched,
+                                     BlkGemmPipelineVer,
+                                     ComputeTypeA,
+                                     ComputeTypeB>;
 
     static constexpr index_t ClusterLengthMPerBlock =
         CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock::At(1);
@@ -347,8 +414,8 @@ struct DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle
     using Block2TileMapElementwise = BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, NPerBlock>;
 
     using GridwiseElementwise =
-        GridwiseElementwise<Tuple<CGridDesc_M_N>,
-                            Tuple<CGridDesc_M_N>,
+        GridwiseElementwise<Tuple<CElementwiseGridDesc_M_N>,
+                            Tuple<CElementwiseGridDesc_M_N>,
                             Tuple<const AccDataType*>,
                             Tuple<EDataType*>,
                             Block2TileMapElementwise,
@@ -366,10 +433,8 @@ struct DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle
 
     // Argument
     using CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock =
-        decltype(GridwiseGemm::MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(CGridDesc_M_N{}));
-
-    using Block2CTileMap =
-        decltype(GridwiseGemm::MakeCBlockClusterAdaptor(CGridDesc_M_N{}, 1, 1, 1));
+        decltype(GridwiseGemm::MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
+            CGridDesc_M_N{}, 1, 1));
 
     struct Argument : public BaseArgument
     {
@@ -395,11 +460,10 @@ struct DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle
             : p_a_grid_{p_out_grid},
               p_b_grid_{p_in_grid},
               p_e_grid_{p_wei_grid},
-              a_grid_desc_kbatch_k0_m_k1_{},
-              b_grid_desc_kbatch_k0_n_k1_{},
+              a_grid_desc_k0_m_k1_{},
+              b_grid_desc_k0_n_k1_{},
               ce_grid_desc_m_n_{},
               c_grid_desc_mblock_mperblock_nblock_nperblock_{},
-              block_2_ctile_map_{},
               compute_ptr_offset_of_batch_{},
               M01_{M01},
               N01_{N01},
@@ -430,7 +494,7 @@ struct DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle
                       begin(output_spatial_lengths_));
 
             const auto descs =
-                conv_to_gemm_transformer
+                conv_to_gemm_transformer_v2
                     .template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<NDimSpatial>(
                         Conv_N_,
                         Conv_K_,
@@ -447,15 +511,34 @@ struct DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle
                         input_right_pads,
                         k_batch_);
 
-            a_grid_desc_kbatch_k0_m_k1_ = descs[I0];
-            b_grid_desc_kbatch_k0_n_k1_ = descs[I1];
-            ce_grid_desc_m_n_           = descs[I2];
+            a_grid_desc_k0_m_k1_ = descs[I0];
+            b_grid_desc_k0_n_k1_ = descs[I1];
+            ce_grid_desc_m_n_    = descs[I2];
+
+            ce_elementwise_grid_desc_m_n_ =
+                conv_to_gemm_transformer_v1
+                    .template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<NDimSpatial>(
+                        Conv_N_,
+                        Conv_K_,
+                        Conv_C_,
+                        input_spatial_lengths_,
+                        filter_spatial_lengths_,
+                        output_spatial_lengths_,
+                        b_g_n_c_wis_strides,
+                        e_g_k_c_xs_strides,
+                        a_g_n_k_wos_strides,
+                        conv_filter_strides,
+                        conv_filter_dilations,
+                        input_left_pads,
+                        input_right_pads,
+                        k_batch_)[I2];
 
-            block_2_ctile_map_ =
-                GridwiseGemm::MakeCBlockClusterAdaptor(ce_grid_desc_m_n_, M01, N01, k_batch_);
             elementwise_block_2_ctile_map_ = Block2TileMapElementwise{
                 ce_grid_desc_m_n_.GetLength(I0), ce_grid_desc_m_n_.GetLength(I1)};
 
+            const index_t GemmM = a_grid_desc_k0_m_k1_.GetLength(I1);
+            const index_t GemmN = b_grid_desc_k0_n_k1_.GetLength(I1);
+
             // A/B/C Batch Stride
             compute_ptr_offset_of_batch_.BatchStrideA_ = a_g_n_k_wos_strides[0];
             compute_ptr_offset_of_batch_.BatchStrideB_ = b_g_n_c_wis_strides[0];
@@ -465,16 +548,11 @@ struct DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle
                                 end(filter_spatial_lengths_),
                                 index_t{1},
                                 std::multiplies<>{});
-
-            if(GridwiseGemm::CheckValidity(a_grid_desc_kbatch_k0_m_k1_,
-                                           b_grid_desc_kbatch_k0_n_k1_,
-                                           ce_grid_desc_m_n_,
-                                           block_2_ctile_map_))
-            {
-                c_grid_desc_mblock_mperblock_nblock_nperblock_ =
-                    GridwiseGemm::MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(
-                        ce_grid_desc_m_n_);
-            }
+            c_grid_desc_mblock_mperblock_nblock_nperblock_ =
+                GridwiseGemm::MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
+                    ce_grid_desc_m_n_,
+                    GridwiseGemm::CalculateMBlock(GemmM),
+                    GridwiseGemm::CalculateNBlock(GemmN));
         }
 
         std::size_t GetWorkspaceSizeBytes() const
@@ -486,12 +564,12 @@ struct DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle
         const BDataType* p_b_grid_;
         EDataType* p_e_grid_;
 
-        AGridDesc_K0_M_K1 a_grid_desc_kbatch_k0_m_k1_;
-        BGridDesc_K0_N_K1 b_grid_desc_kbatch_k0_n_k1_;
+        AGridDesc_K0_M_K1 a_grid_desc_k0_m_k1_;
+        BGridDesc_K0_N_K1 b_grid_desc_k0_n_k1_;
         CGridDesc_M_N ce_grid_desc_m_n_;
+        CElementwiseGridDesc_M_N ce_elementwise_grid_desc_m_n_;
         CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock c_grid_desc_mblock_mperblock_nblock_nperblock_;
 
-        Block2CTileMap block_2_ctile_map_;
         Block2TileMapElementwise elementwise_block_2_ctile_map_;
 
         // for computing batch offset
@@ -525,96 +603,676 @@ struct DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle
 
         void ShowInfo(const Argument& arg)
         {
-            std::cout << "arg.a_grid_desc_kbatch_k0_m_k1_{"
-                      << arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I0) << ", "
-                      << arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I1) << ", "
-                      << arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I2) << ", "
-                      << arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I3) << "}" << std::endl;
-
-            std::cout << "arg.b_grid_desc_kbatch_k0_n_k1_{"
-                      << arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I0) << ", "
-                      << arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I1) << ", "
-                      << arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I2) << ", "
-                      << arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I3) << "}" << std::endl;
+            std::cout << "arg.a_grid_desc_k0_m_k1_{" << arg.a_grid_desc_k0_m_k1_.GetLength(I0)
+                      << ", " << arg.a_grid_desc_k0_m_k1_.GetLength(I1) << ", "
+                      << arg.a_grid_desc_k0_m_k1_.GetLength(I2) << "}" << std::endl;
+
+            std::cout << "arg.b_grid_desc_k0_n_k1_{" << arg.b_grid_desc_k0_n_k1_.GetLength(I0)
+                      << ", " << arg.b_grid_desc_k0_n_k1_.GetLength(I1) << ", "
+                      << arg.b_grid_desc_k0_n_k1_.GetLength(I2) << "}" << std::endl;
 
             std::cout << "arg.ce_grid_desc_m_n_{" << arg.ce_grid_desc_m_n_.GetLength(I0) << ", "
                       << arg.ce_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
         }
 
-        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
+        float RunGemmV3(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
         {
-            if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_kbatch_k0_m_k1_,
-                                            arg.b_grid_desc_kbatch_k0_n_k1_,
-                                            arg.ce_grid_desc_m_n_,
-                                            arg.block_2_ctile_map_))
+            const index_t GemmM = arg.a_grid_desc_k0_m_k1_.GetLength(I1);
+            const index_t GemmN = arg.b_grid_desc_k0_n_k1_.GetLength(I1);
+            const index_t GemmK =
+                arg.a_grid_desc_k0_m_k1_.GetLength(I0) * arg.a_grid_desc_k0_m_k1_.GetLength(I2);
+
+            AccDataType* p_c_grid = type_convert<AccDataType*>(arg.p_workspace_);
+
+            // nullptr for output, will be set after workspace set
+            typename GridwiseGemm::Argument gemm_arg{arg.p_a_grid_,
+                                                     arg.p_b_grid_,
+                                                     p_c_grid,
+                                                     GemmM,
+                                                     GemmN,
+                                                     GemmK,
+                                                     I0,
+                                                     I0,
+                                                     I0,
+                                                     arg.k_batch_};
+
+            index_t gdx, gdy, gdz;
+            std::tie(gdx, gdy, gdz) = GridwiseGemm::CalculateGridSize(
+                gemm_arg.M, gemm_arg.N, gemm_arg.KBatch, arg.Conv_G_ / NumBatchToMerge);
+
+            float ave_time = 0;
+
+            index_t k_grain = gemm_arg.KBatch * KPerBlock;
+            index_t K_split = (gemm_arg.K + k_grain - 1) / k_grain * (KPerBlock);
+
+            const bool has_main_k_block_loop = GridwiseGemm::CalculateHasMainKBlockLoop(K_split);
+
+            const auto num_k_per_block =
+                arg.a_grid_desc_k0_m_k1_.GetLength(Number<0>{}) / gemm_arg.KBatch;
+
+            const auto clear_workspace = [&]() {
+                hip_check_error(hipMemsetAsync(
+                    gemm_arg.p_c_grid, 0, arg.GetWorkspaceSizeBytes(), stream_config.stream_id_));
+            };
+
+            const auto Run = [&](const auto& kernel) {
+                if(stream_config.flush_cache)
+                {
+                    typename GridwiseGemm::Argument gemm_arg_ = gemm_arg;
+                    ck::utility::RotatingMemWrapper<typename GridwiseGemm::Argument> rotating_mem(
+                        gemm_arg_,
+                        stream_config.rotating_count,
+                        gemm_arg_.M * gemm_arg_.K * sizeof(ADataType),
+                        gemm_arg_.K * gemm_arg_.N * sizeof(BDataType));
+                    rotating_mem.Print();
+
+                    auto run_flush_cache = [&]() {
+                        // flush icache
+                        ck::utility::flush_icache();
+                        // rotating mem
+                        rotating_mem.Next();
+                        clear_workspace();
+                    };
+
+                    ave_time = ck::utility::launch_and_time_kernel_with_preprocess<false>(
+                        stream_config,
+                        run_flush_cache,
+                        kernel,
+                        dim3(gdx, gdy, gdz),
+                        dim3(BlockSize),
+                        0,
+                        gemm_arg_,
+                        arg.a_grid_desc_k0_m_k1_,
+                        arg.b_grid_desc_k0_n_k1_,
+                        arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
+                        arg.compute_ptr_offset_of_batch_,
+                        num_k_per_block);
+                }
+                else
+                {
+                    ave_time = launch_and_time_kernel_with_preprocess(
+                        stream_config,
+                        clear_workspace,
+                        kernel,
+                        dim3(gdx, gdy, gdz),
+                        dim3(BlockSize),
+                        0,
+                        gemm_arg,
+                        arg.a_grid_desc_k0_m_k1_,
+                        arg.b_grid_desc_k0_n_k1_,
+                        arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
+                        arg.compute_ptr_offset_of_batch_,
+                        num_k_per_block);
+                }
+            };
+
+            constexpr index_t minimum_occupancy =
+                BlkGemmPipeSched == BlockGemmPipelineScheduler::Intrawave ? 1 : 2;
+
+            if(has_main_k_block_loop)
+            {
+                // Tail number always full
+                if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v1 ||
+                             BlkGemmPipelineVer == BlockGemmPipelineVersion::v3)
+                {
+                    if(gemm_arg.KBatch > 1)
+                    {
+                        const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                            GridwiseGemm,
+                            remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                            remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                            remove_reference_t<
+                                DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                            ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                            NumBatchToMerge,
+                            true,
+                            InMemoryDataOperationEnum::AtomicAdd,
+                            minimum_occupancy>;
+                        Run(kernel);
+                    }
+                    else
+                    {
+                        const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                            GridwiseGemm,
+                            remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                            remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                            remove_reference_t<
+                                DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                            ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                            NumBatchToMerge,
+                            true,
+                            InMemoryDataOperationEnum::Set,
+                            minimum_occupancy>;
+                        Run(kernel);
+                    }
+                }
+                // Tail number could be One to Seven
+                else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v2)
+                {
+                    if(gemm_arg.KBatch > 1)
+                    {
+                        if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::One)
+                        {
+                            const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                GridwiseGemm,
+                                remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                                remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                                remove_reference_t<
+                                    DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                                ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                NumBatchToMerge,
+                                true,
+                                InMemoryDataOperationEnum::AtomicAdd,
+                                minimum_occupancy,
+                                TailNumber::One>;
+                            Run(kernel);
+                        }
+                        else if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                                TailNumber::Full)
+                        {
+                            const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                GridwiseGemm,
+                                remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                                remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                                remove_reference_t<
+                                    DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                                ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                NumBatchToMerge,
+                                true,
+                                InMemoryDataOperationEnum::AtomicAdd,
+                                minimum_occupancy,
+                                TailNumber::Full>;
+                            Run(kernel);
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 2)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Two)
+                            {
+                                const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                                    remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                                    remove_reference_t<
+                                        DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                                    ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                    NumBatchToMerge,
+                                    true,
+                                    InMemoryDataOperationEnum::AtomicAdd,
+                                    minimum_occupancy,
+                                    TailNumber::Two>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 3)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                               TailNumber::Three)
+                            {
+                                const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                                    remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                                    remove_reference_t<
+                                        DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                                    ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                    NumBatchToMerge,
+                                    true,
+                                    InMemoryDataOperationEnum::AtomicAdd,
+                                    minimum_occupancy,
+                                    TailNumber::Three>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 4)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                               TailNumber::Four)
+                            {
+                                const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                                    remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                                    remove_reference_t<
+                                        DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                                    ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                    NumBatchToMerge,
+                                    true,
+                                    InMemoryDataOperationEnum::AtomicAdd,
+                                    minimum_occupancy,
+                                    TailNumber::Four>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 5)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                               TailNumber::Five)
+                            {
+                                const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                                    remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                                    remove_reference_t<
+                                        DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                                    ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                    NumBatchToMerge,
+                                    true,
+                                    InMemoryDataOperationEnum::AtomicAdd,
+                                    minimum_occupancy,
+                                    TailNumber::Five>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 6)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Six)
+                            {
+                                const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                                    remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                                    remove_reference_t<
+                                        DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                                    ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                    NumBatchToMerge,
+                                    true,
+                                    InMemoryDataOperationEnum::AtomicAdd,
+                                    minimum_occupancy,
+                                    TailNumber::Six>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 7)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                               TailNumber::Seven)
+                            {
+                                const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                                    remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                                    remove_reference_t<
+                                        DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                                    ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                    NumBatchToMerge,
+                                    true,
+                                    InMemoryDataOperationEnum::AtomicAdd,
+                                    minimum_occupancy,
+                                    TailNumber::Seven>;
+                                Run(kernel);
+                            }
+                        }
+                    }
+                    else
+                    {
+                        if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::One)
+                        {
+                            const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                GridwiseGemm,
+                                remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                                remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                                remove_reference_t<
+                                    DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                                ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                NumBatchToMerge,
+                                true,
+                                InMemoryDataOperationEnum::Set,
+                                minimum_occupancy,
+                                TailNumber::One>;
+                            Run(kernel);
+                        }
+                        else if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                                TailNumber::Full)
+                        {
+                            const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                GridwiseGemm,
+                                remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                                remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                                remove_reference_t<
+                                    DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                                ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                NumBatchToMerge,
+                                true,
+                                InMemoryDataOperationEnum::Set,
+                                minimum_occupancy,
+                                TailNumber::Full>;
+                            Run(kernel);
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 2)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Two)
+                            {
+                                const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                                    remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                                    remove_reference_t<
+                                        DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                                    ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                    NumBatchToMerge,
+                                    true,
+                                    InMemoryDataOperationEnum::Set,
+                                    minimum_occupancy,
+                                    TailNumber::Two>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 3)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                               TailNumber::Three)
+                            {
+                                const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                                    remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                                    remove_reference_t<
+                                        DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                                    ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                    NumBatchToMerge,
+                                    true,
+                                    InMemoryDataOperationEnum::Set,
+                                    minimum_occupancy,
+                                    TailNumber::Three>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 4)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                               TailNumber::Four)
+                            {
+                                const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                                    remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                                    remove_reference_t<
+                                        DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                                    ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                    NumBatchToMerge,
+                                    true,
+                                    InMemoryDataOperationEnum::Set,
+                                    minimum_occupancy,
+                                    TailNumber::Four>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 5)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                               TailNumber::Five)
+                            {
+                                const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                                    remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                                    remove_reference_t<
+                                        DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                                    ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                    NumBatchToMerge,
+                                    true,
+                                    InMemoryDataOperationEnum::Set,
+                                    minimum_occupancy,
+                                    TailNumber::Five>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 6)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Six)
+                            {
+                                const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                                    remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                                    remove_reference_t<
+                                        DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                                    ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                    NumBatchToMerge,
+                                    true,
+                                    InMemoryDataOperationEnum::Set,
+                                    minimum_occupancy,
+                                    TailNumber::Six>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 7)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                               TailNumber::Seven)
+                            {
+                                const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                                    remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                                    remove_reference_t<
+                                        DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                                    ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                    NumBatchToMerge,
+                                    true,
+                                    InMemoryDataOperationEnum::Set,
+                                    minimum_occupancy,
+                                    TailNumber::Seven>;
+                                Run(kernel);
+                            }
+                        }
+                    }
+                }
+                // Tail number could be Odd or Even
+                else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v4)
+                {
+                    if(gemm_arg.KBatch > 1)
+                    {
+                        if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Odd)
+                        {
+                            const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3_2lds<
+                                GridwiseGemm,
+                                remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                                remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                                remove_reference_t<
+                                    DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                                ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                NumBatchToMerge,
+                                true,
+                                InMemoryDataOperationEnum::AtomicAdd,
+                                minimum_occupancy,
+                                TailNumber::Odd>;
+                            Run(kernel);
+                        }
+                        else
+                        {
+                            const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3_2lds<
+                                GridwiseGemm,
+                                remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                                remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                                remove_reference_t<
+                                    DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                                ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                NumBatchToMerge,
+                                true,
+                                InMemoryDataOperationEnum::AtomicAdd,
+                                minimum_occupancy,
+                                TailNumber::Even>;
+                            Run(kernel);
+                        }
+                    }
+                    else
+                    {
+                        if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Odd)
+                        {
+                            const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3_2lds<
+                                GridwiseGemm,
+                                remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                                remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                                remove_reference_t<
+                                    DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                                ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                NumBatchToMerge,
+                                true,
+                                InMemoryDataOperationEnum::Set,
+                                minimum_occupancy,
+                                TailNumber::Odd>;
+                            Run(kernel);
+                        }
+                        else
+                        {
+                            const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3_2lds<
+                                GridwiseGemm,
+                                remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                                remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                                remove_reference_t<
+                                    DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                                ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                NumBatchToMerge,
+                                true,
+                                InMemoryDataOperationEnum::Set,
+                                minimum_occupancy,
+                                TailNumber::Even>;
+                            Run(kernel);
+                        }
+                    }
+                }
+                else
+                {
+                    if(gemm_arg.KBatch > 1)
+                    {
+                        if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Odd)
+                        {
+                            const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                GridwiseGemm,
+                                remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                                remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                                remove_reference_t<
+                                    DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                                ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                NumBatchToMerge,
+                                true,
+                                InMemoryDataOperationEnum::AtomicAdd,
+                                minimum_occupancy,
+                                TailNumber::Odd>;
+                            Run(kernel);
+                        }
+                        else
+                        {
+                            const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                GridwiseGemm,
+                                remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                                remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                                remove_reference_t<
+                                    DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                                ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                NumBatchToMerge,
+                                true,
+                                InMemoryDataOperationEnum::AtomicAdd,
+                                minimum_occupancy,
+                                TailNumber::Even>;
+                            Run(kernel);
+                        }
+                    }
+                    else
+                    {
+                        if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Odd)
+                        {
+                            const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                GridwiseGemm,
+                                remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                                remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                                remove_reference_t<
+                                    DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                                ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                NumBatchToMerge,
+                                true,
+                                InMemoryDataOperationEnum::Set,
+                                minimum_occupancy,
+                                TailNumber::Odd>;
+                            Run(kernel);
+                        }
+                        else
+                        {
+                            const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                GridwiseGemm,
+                                remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                                remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                                remove_reference_t<
+                                    DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                                ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                NumBatchToMerge,
+                                true,
+                                InMemoryDataOperationEnum::Set,
+                                minimum_occupancy,
+                                TailNumber::Even>;
+                            Run(kernel);
+                        }
+                    }
+                }
+            }
+            else
             {
-                throw std::runtime_error(
-                    "wrong! GridwiseGemm_km_kn_m0m1n0n1_xdlops_v3r1 has invalid setting");
+                // Tail number always 1
+                if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v1)
+                {
+                    if(gemm_arg.KBatch > 1)
+                    {
+                        const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                            GridwiseGemm,
+                            remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                            remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                            remove_reference_t<
+                                DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                            ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                            NumBatchToMerge,
+                            false,
+                            InMemoryDataOperationEnum::AtomicAdd,
+                            minimum_occupancy>;
+                        Run(kernel);
+                    }
+                    else
+                    {
+                        const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                            GridwiseGemm,
+                            remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                            remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                            remove_reference_t<
+                                DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                            ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                            NumBatchToMerge,
+                            false,
+                            InMemoryDataOperationEnum::Set,
+                            minimum_occupancy>;
+                        Run(kernel);
+                    }
+                }
             }
 
-            const auto K0                     = arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I1);
-            const bool has_main_k0_block_loop = GridwiseGemm::CalculateHasMainK0BlockLoop(K0);
-
-            auto launch_gemm_kernel = [&](auto has_main_k_block_loop) {
-                AccDataType* p_c_grid = type_convert<AccDataType*>(arg.p_workspace_);
-                const index_t grid_size =
-                    arg.block_2_ctile_map_.CalculateGridSize(arg.ce_grid_desc_m_n_) * arg.Conv_G_;
-
-                constexpr bool has_main_loop = has_main_k_block_loop.value;
-
-                auto preprocess = [&]() {
-                    hip_check_error(hipMemsetAsync(
-                        p_c_grid, 0, arg.GetWorkspaceSizeBytes(), stream_config.stream_id_));
-                };
-
-                const auto kernel = kernel_batched_gemm_xdlops_bwd_weight<
-                    GridwiseGemm,
-                    ADataType,
-                    BDataType,
-                    AccDataType,
-                    OutElementwiseOperation,
-                    InElementwiseOperation,
-                    element_wise::PassThrough,
-                    remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
-                    remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
-                    remove_reference_t<DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
-                    remove_reference_t<DeviceOp::Block2CTileMap>,
-                    ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
-                    has_main_loop>;
-
-                return launch_and_time_kernel_with_preprocess(
-                    stream_config,
-                    preprocess,
-                    kernel,
-                    dim3(grid_size),
-                    dim3(BlockSize),
-                    0,
-                    arg.p_a_grid_,
-                    arg.p_b_grid_,
-                    p_c_grid,
-                    arg.a_element_op_,
-                    arg.b_element_op_,
-                    element_wise::PassThrough{},
-                    arg.Conv_G_,
-                    arg.a_grid_desc_kbatch_k0_m_k1_,
-                    arg.b_grid_desc_kbatch_k0_n_k1_,
-                    arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
-                    arg.block_2_ctile_map_,
-                    arg.compute_ptr_offset_of_batch_);
-            };
+            return ave_time;
+        }
 
+        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
+        {
             auto launch_elementwise_kernel = [&]() {
                 const AccDataType* p_c_grid = type_convert<const AccDataType*>(arg.p_workspace_);
-                const index_t grid_size =
-                    arg.elementwise_block_2_ctile_map_.CalculateGridSize(arg.ce_grid_desc_m_n_) *
-                    arg.Conv_G_;
+                const index_t grid_size     = arg.elementwise_block_2_ctile_map_.CalculateGridSize(
+                                              arg.ce_elementwise_grid_desc_m_n_) *
+                                          arg.Conv_G_;
 
                 std::array<index_t, I1> in_out_batch_strides = {
                     arg.compute_ptr_offset_of_batch_.BatchStrideC_};
 
                 const auto kernel = kernel_batched_elementwise<GridwiseElementwise,
-                                                               ck::Tuple<CGridDesc_M_N>,
-                                                               ck::Tuple<CGridDesc_M_N>,
+                                                               ck::Tuple<CElementwiseGridDesc_M_N>,
+                                                               ck::Tuple<CElementwiseGridDesc_M_N>,
                                                                ck::Tuple<const AccDataType*>,
                                                                ck::Tuple<EDataType*>,
                                                                Block2TileMapElementwise,
@@ -627,8 +1285,8 @@ struct DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle
                                               dim3(grid_size),
                                               dim3(BlockSize),
                                               0,
-                                              make_tuple(arg.ce_grid_desc_m_n_),
-                                              make_tuple(arg.ce_grid_desc_m_n_),
+                                              make_tuple(arg.ce_elementwise_grid_desc_m_n_),
+                                              make_tuple(arg.ce_elementwise_grid_desc_m_n_),
                                               make_tuple(p_c_grid),
                                               make_tuple(arg.p_e_grid_),
                                               arg.elementwise_block_2_ctile_map_,
@@ -638,16 +1296,7 @@ struct DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle
                                               in_out_batch_strides);
             };
 
-            float avg_time = 0;
-            if(has_main_k0_block_loop)
-            {
-                avg_time = launch_gemm_kernel(integral_constant<bool, true>{});
-            }
-            else
-            {
-                avg_time = launch_gemm_kernel(integral_constant<bool, false>{});
-            }
-
+            float avg_time = RunGemmV3(arg, stream_config);
             avg_time += launch_elementwise_kernel();
             return avg_time;
         }
@@ -667,6 +1316,23 @@ struct DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle
 
     static bool IsSupportedArgument(const Argument& arg)
     {
+        const index_t GemmM = arg.a_grid_desc_k0_m_k1_.GetLength(I1);
+        const index_t GemmN = arg.b_grid_desc_k0_n_k1_.GetLength(I1);
+        const index_t GemmK =
+            arg.a_grid_desc_k0_m_k1_.GetLength(I0) * arg.a_grid_desc_k0_m_k1_.GetLength(I2);
+
+        typename GridwiseGemm::Argument gemm_arg{
+            nullptr, nullptr, nullptr, GemmM, GemmN, GemmK, I0, I0, I0, arg.k_batch_};
+
+        const auto num_k_loop = gemm_arg.AK0 / (KPerBlock / K1);
+        if constexpr(BlkGemmPipelineVer != BlockGemmPipelineVersion::v1)
+        {
+            if(num_k_loop <= GridwiseGemm::BlockwiseGemmPipe::PrefetchStages)
+            {
+                return false;
+            }
+        }
+
         // Check this here, it allows to use other instances from factory even
         // if workspace is not allocated
         if(!arg.p_workspace_)
@@ -723,10 +1389,38 @@ struct DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle
             }
         }
 
+        if constexpr(NumBatchToMerge > 1)
+        {
+            // support only if whole M and N can be proccessed on one block
+            if(!(GemmM <= MPerBlock && GemmN <= NPerBlock))
+            {
+                return false;
+            }
+            if(!(arg.Conv_C_ == 1 && arg.Conv_K_ == 1))
+            {
+                return false;
+            }
+            if(arg.Conv_G_ % NumBatchToMerge != 0)
+            {
+                return false;
+            }
+        }
+
+        if(!(arg.Conv_C_ % BBlockTransferSrcScalarPerVector == 0 &&
+             arg.Conv_K_ % ABlockTransferSrcScalarPerVector == 0))
+        {
+            if(!(arg.Conv_K_ == 1 && arg.compute_ptr_offset_of_batch_.BatchStrideA_ == 1))
+            {
+                return false;
+            }
+            if(!(arg.Conv_C_ == 1 && arg.compute_ptr_offset_of_batch_.BatchStrideB_ == 1))
+            {
+                return false;
+            }
+        }
+
         // vector load A/B matrix from global memory
-        if(!(ABlockTransferSrcVectorDim == 2 && BBlockTransferSrcVectorDim == 2 &&
-             arg.Conv_K_ % ABlockTransferSrcScalarPerVector == 0 &&
-             arg.Conv_C_ % BBlockTransferSrcScalarPerVector == 0))
+        if(!(ABlockTransferSrcVectorDim == 1 && BBlockTransferSrcVectorDim == 1))
         {
             return false;
         }
@@ -737,11 +1431,7 @@ struct DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle
             return false;
         }
 
-        // Gridwise GEMM size
-        return GridwiseGemm::CheckValidity(arg.a_grid_desc_kbatch_k0_m_k1_,
-                                           arg.b_grid_desc_kbatch_k0_n_k1_,
-                                           arg.ce_grid_desc_m_n_,
-                                           arg.block_2_ctile_map_);
+        return true;
     }
 
     bool IsSupportedArgument(const BaseArgument* p_arg) override
@@ -840,13 +1530,24 @@ struct DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle
     {
         auto str = std::stringstream();
 
+        std::map<BlockGemmPipelineScheduler, std::string> BlkGemmPipelineSchedulerToString{
+            {BlockGemmPipelineScheduler::Intrawave, "Intrawave"},
+            {BlockGemmPipelineScheduler::Interwave, "Interwave"}};
+
+        std::map<BlockGemmPipelineVersion, std::string> BlkGemmPipelineVersionToString{
+            {BlockGemmPipelineVersion::v1, "v1"},
+            {BlockGemmPipelineVersion::v2, "v2"},
+            {BlockGemmPipelineVersion::v3, "v3"},
+            {BlockGemmPipelineVersion::v4, "v4"},
+            {BlockGemmPipelineVersion::v5, "v5"}};
+
         // clang-format off
         str << "DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle"
             << "<"
             << BlockSize << ", "
             << MPerBlock << ", "
             << NPerBlock << ", "
-            << K0PerBlock << ", "
+            << KPerBlock << ", "
             << getConvBackwardWeightSpecializationString(ConvBackwardWeightSpecialization) << ", "
             << K1 << ", "
             << MXdlPerWave << ", "
@@ -857,7 +1558,12 @@ struct DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle
             << BBlockTransferDstScalarPerVector_K1 << ", "
             << CShuffleMXdlPerWavePerShuffle << ", "
             << CShuffleNXdlPerWavePerShuffle << ", "
-            << CBlockTransferScalarPerVector_NWaveNPerXdl
+            << CBlockTransferScalarPerVector_NWaveNPerXdl << ", "
+            << "BlkGemmPipelineScheduler: "
+            << BlkGemmPipelineSchedulerToString[BlkGemmPipeSched] << ", "
+            << "BlkGemmPipelineVersion: "
+            << BlkGemmPipelineVersionToString[BlkGemmPipelineVer] << ", "
+            << NumBatchToMerge
             << ">";
         // clang-format on
 

include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_multi_abd_xdl_fixed_nk.hpp

@@ -45,8 +45,7 @@ __global__ void
                                          const BElementwiseOperation b_element_op,
                                          const CDEElementwiseOperation cde_element_op)
 {
-#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
-    defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__))
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx9__))
     __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
 
     const index_t KBatch = 1;

include/ck/tensor_operation/gpu/grid/gridwise_fpAintB_gemm_wmma.hpp

@@ -50,8 +50,7 @@ __global__ void
                                  const CElementwiseOperation c_element_op,
                                  const Block2CTileMap block_2_ctile_map)
 {
-#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx1100__) || defined(__gfx1101__) || \
-    defined(__gfx1102__))
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx11__))
     __shared__ char p_shared[GridwiseGemm::SharedMemTrait::lds_size];
 
     GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid,
@@ -80,7 +79,7 @@ __global__ void
     ignore = b_element_op;
     ignore = c_element_op;
     ignore = block_2_ctile_map;
-#endif // end of if (defined(__gfx1100__))
+#endif // end of if (defined(__gfx11__))
 }
 
 // Assume B is Col-Major

include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdl_cshuffle_bwd_weight_v3.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/utility/common_header.hpp"
+#include "ck/tensor_description/multi_index_transform_helper.hpp"
+#include "ck/tensor_description/tensor_descriptor.hpp"
+#include "ck/tensor_description/tensor_descriptor_helper.hpp"
+#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_selector.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v6r1.hpp"
+#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+namespace ck {
+
+template <typename ALayout,
+          typename BLayout,
+          typename CLayout,
+          typename ADataType,
+          typename BDataType,
+          typename AccDataType,
+          typename CShuffleDataType,
+          typename CDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CElementwiseOperation,
+          tensor_operation::device::GemmSpecialization GemmSpec,
+          index_t BlockSize,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t KPerBlock,
+          index_t AK1Value,
+          index_t BK1Value,
+          index_t MPerXdl,
+          index_t NPerXdl,
+          index_t MXdlPerWave,
+          index_t NXdlPerWave,
+          typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
+          typename ABlockTransferThreadClusterArrangeOrder,
+          typename ABlockTransferSrcAccessOrder,
+          index_t ABlockTransferSrcVectorDim,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t ABlockTransferDstScalarPerVector_AK1,
+          bool AThreadTransferSrcResetCoordinateAfterRun,
+          index_t ABlockLdsExtraM,
+          typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
+          typename BBlockTransferThreadClusterArrangeOrder,
+          typename BBlockTransferSrcAccessOrder,
+          index_t BBlockTransferSrcVectorDim,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t BBlockTransferDstScalarPerVector_BK1,
+          bool BThreadTransferSrcResetCoordinateAfterRun,
+          index_t BBlockLdsExtraN,
+          index_t CShuffleMXdlPerWavePerShuffle,
+          index_t CShuffleNXdlPerWavePerShuffle,
+          typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+          index_t CShuffleBlockTransferScalarPerVector_NPerBlock,
+          BlockGemmPipelineScheduler BlkGemmPipeSched = BlockGemmPipelineScheduler::Intrawave,
+          BlockGemmPipelineVersion BlkGemmPipelineVer = BlockGemmPipelineVersion::v4,
+          typename ComputeTypeA                       = CDataType,
+          typename ComputeTypeB                       = ComputeTypeA>
+struct GridwiseGemm_xdl_cshuffle_v3
+{
+    static constexpr auto I0 = Number<0>{};
+    static constexpr auto I1 = Number<1>{};
+    static constexpr auto I2 = Number<2>{};
+    static constexpr auto I3 = Number<3>{};
+    static constexpr auto I4 = Number<4>{};
+    static constexpr auto I5 = Number<5>{};
+    static constexpr auto I6 = Number<6>{};
+    static constexpr auto I7 = Number<7>{};
+
+    // K1 should be Number<...>
+    static constexpr auto AK0Number = Number<KPerBlock / AK1Value>{};
+    static constexpr auto BK0Number = Number<KPerBlock / BK1Value>{};
+    static constexpr auto AK1Number = Number<AK1Value>{};
+    static constexpr auto BK1Number = Number<BK1Value>{};
+
+    static constexpr index_t KPack =
+        math::max(math::lcm(AK1Number, BK1Number),
+                  MfmaSelector<ComputeTypeA, MPerXdl, NPerXdl>::selected_mfma.k_per_blk);
+
+    using ThisThreadBlock = ThisThreadBlock<BlockSize>;
+
+    __host__ static auto CalculateGridSize(index_t M, index_t N, index_t KBatch, index_t Batch)
+    {
+        return std::make_tuple(Block2CTileMap::CalculateGridSize(M, N), KBatch, Batch);
+    }
+
+    __host__ static auto CalculateMPadded(index_t M)
+    {
+        return math::integer_least_multiple(M, MPerBlock);
+    }
+
+    __host__ static auto CalculateNPadded(index_t N)
+    {
+        return math::integer_least_multiple(N, NPerBlock);
+    }
+
+    __host__ static auto CalculateKPadded(index_t K)
+    {
+        return math::integer_divide_ceil(K, KPerBlock) * KPerBlock;
+    }
+
+    __host__ static auto CalculateAK0Padded(index_t K, index_t K_Batch = 1)
+    {
+        auto K_t = K_Batch * KPerBlock;
+        return (K + K_t - 1) / K_t * (KPerBlock / AK1Value);
+    }
+
+    __host__ static auto CalculateBK0Padded(index_t K, index_t K_Batch = 1)
+    {
+        auto K_t = K_Batch * KPerBlock;
+        return (K + K_t - 1) / K_t * (KPerBlock / BK1Value);
+    }
+
+    __host__ static auto CalculateKPadded(index_t K, index_t K_Batch = 1)
+    {
+        auto K_t = K_Batch * KPerBlock;
+        return (K + K_t - 1) / K_t * KPerBlock;
+    }
+
+    __host__ static auto CalculateKRead(index_t K, index_t K_Batch = 1)
+    {
+        constexpr auto KReadVec = math::lcm(AK1Number, BK1Number);
+        auto K_t                = K_Batch * KReadVec;
+        return (K + K_t - 1) / K_t * KReadVec;
+    }
+
+    __host__ static auto CalculateMBlock(index_t M)
+    {
+        return math::integer_divide_ceil(M, MPerBlock);
+    }
+
+    __host__ static auto CalculateNBlock(index_t N)
+    {
+        return math::integer_divide_ceil(N, NPerBlock);
+    }
+
+    template <index_t MNXdlPerWave, index_t MNWaves, index_t MNPerXdl, typename TileDesc_K0_MN_K1>
+    __host__ __device__ static constexpr auto MakeGemmMmaTileDescriptor(const TileDesc_K0_MN_K1&)
+    {
+        constexpr index_t K0 = TileDesc_K0_MN_K1{}.GetLength(Number<0>{});
+        constexpr index_t K1 = TileDesc_K0_MN_K1{}.GetLength(Number<2>{});
+
+        return transform_tensor_descriptor(
+            TileDesc_K0_MN_K1{},
+            make_tuple(make_merge_transform_v3_division_mod(make_tuple(Number<K0>{}, Number<K1>{})),
+                       make_unmerge_transform(make_tuple(
+                           Number<MNXdlPerWave>{}, Number<MNWaves>{}, Number<MNPerXdl>{}))),
+            make_tuple(Sequence<0, 2>{}, Sequence<1>{}),
+            make_tuple(Sequence<3>{}, Sequence<0, 1, 2>{}));
+    }
+
+    template <typename ABlockDesc_AK0_M_AK1>
+    __host__ __device__ static constexpr auto
+    MakeAMmaTileDescriptor_M0_M1_M2_K(const ABlockDesc_AK0_M_AK1&)
+    {
+        constexpr index_t MWaves = MPerBlock / (MXdlPerWave * MPerXdl);
+
+        return MakeGemmMmaTileDescriptor<MXdlPerWave, MWaves, MPerXdl>(ABlockDesc_AK0_M_AK1{});
+    }
+
+    template <typename BBlockDesc_BK0_N_BK1>
+    __host__ __device__ static constexpr auto
+    MakeBMmaTileDescriptor_N0_N1_N2_K(const BBlockDesc_BK0_N_BK1&)
+    {
+        constexpr index_t NWaves = NPerBlock / (NXdlPerWave * NPerXdl);
+
+        return MakeGemmMmaTileDescriptor<NXdlPerWave, NWaves, NPerXdl>(BBlockDesc_BK0_N_BK1{});
+    }
+
+    struct Problem
+    {
+        __host__ Problem(index_t M_,
+                         index_t N_,
+                         index_t K_,
+                         index_t StrideA_,
+                         index_t StrideB_,
+                         index_t StrideC_,
+                         index_t KBatch_)
+            : M{M_},
+              N{N_},
+              K{K_},
+              StrideA{StrideA_},
+              StrideB{StrideB_},
+              StrideC{StrideC_},
+              KBatch{KBatch_},
+              MPadded{CalculateMPadded(M_)},
+              NPadded{CalculateNPadded(N_)},
+              KRead{CalculateKRead(K_, KBatch_)},
+              KPadded{CalculateKPadded(K_, KBatch_)},
+              AK0{CalculateAK0Padded(K_, KBatch_)},
+              BK0{CalculateBK0Padded(K_, KBatch_)},
+              MBlock{CalculateMBlock(M_)},
+              NBlock{CalculateNBlock(N_)}
+        {
+        }
+
+        __host__ void Print() const
+        {
+            std::cout << "problem {"
+                      << "M:" << M << ", "
+                      << "N:" << N << ", "
+                      << "K:" << K << ", "
+                      << "SA:" << StrideA << ", "
+                      << "SB:" << StrideB << ", "
+                      << "SC:" << StrideC << ", "
+                      << "MP:" << MPadded << ", "
+                      << "NP:" << NPadded << ", "
+                      << "KRead:" << KRead << ", "
+                      << "KP:" << KPadded << ", "
+                      << "AK0:" << AK0 << ", "
+                      << "BK0:" << BK0 << ", "
+                      << "MBlock: " << MBlock << ", "
+                      << "NBlock: " << NBlock << "}" << std::endl;
+        }
+
+        index_t M;
+        index_t N;
+        index_t K;
+        index_t StrideA;
+        index_t StrideB;
+        index_t StrideC;
+        index_t KBatch;
+        index_t MPadded;
+        index_t NPadded;
+        index_t KRead;
+        index_t KPadded;
+        index_t AK0;
+        index_t BK0;
+        index_t MBlock;
+        index_t NBlock;
+    };
+
+    // Argument
+    struct Argument : public tensor_operation::device::BaseArgument, public Problem
+    {
+        __host__ Argument(const ADataType* p_a_grid_,
+                          const BDataType* p_b_grid_,
+                          CDataType* p_c_grid_,
+                          index_t M_,
+                          index_t N_,
+                          index_t K_,
+                          index_t StrideA_,
+                          index_t StrideB_,
+                          index_t StrideC_,
+                          index_t k_batch_)
+            : Problem{M_, N_, K_, StrideA_, StrideB_, StrideC_, k_batch_},
+              p_a_grid{p_a_grid_},
+              p_b_grid{p_b_grid_},
+              p_c_grid{p_c_grid_}
+        {
+        }
+
+        const ADataType* p_a_grid;
+        const BDataType* p_b_grid;
+        CDataType* p_c_grid;
+    };
+
+    __device__ static constexpr auto GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1()
+    {
+        // A matrix in LDS memory, dst of blockwise copy
+        if constexpr(ABlockLdsExtraM)
+        {
+            return make_naive_tensor_descriptor(
+                make_tuple(AK0Number, Number<MPerBlock>{}, AK1Number),
+                make_tuple(AK1Number, Number<KPerBlock + ABlockLdsExtraM>{}, I1));
+        }
+        // xor tensor transformation request more unnecessary vgpr usage, would cause register spill
+        // in some cases.
+        else if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)
+        {
+            constexpr auto MLdsLayer        = 32 * 4 / KPerBlock / sizeof(ADataType) < 1
+                                                  ? 1
+                                                  : 32 * 4 / KPerBlock / sizeof(ADataType);
+            constexpr auto a_lds_block_desc = make_naive_tensor_descriptor(
+                make_tuple(
+                    AK0Number * Number<MLdsLayer>{}, Number<MPerBlock / MLdsLayer>{}, AK1Number),
+                make_tuple(AK1Number, Number<KPerBlock * MLdsLayer>{}, I1));
+
+            constexpr auto a_lds_block_desc_permuted = transform_tensor_descriptor(
+                a_lds_block_desc,
+                make_tuple(make_xor_with_modulo_transform(make_tuple(
+                               Number<MPerBlock / MLdsLayer>{}, Number<AK0Number * MLdsLayer>{})),
+                           make_pass_through_transform(AK1Number)),
+                make_tuple(Sequence<1, 0>{}, Sequence<2>{}),
+                make_tuple(Sequence<1, 0>{}, Sequence<2>{}));
+
+            constexpr auto a_lds_block_desc_ak0_mldslayer_m_ak1 = transform_tensor_descriptor(
+                a_lds_block_desc_permuted,
+                make_tuple(make_unmerge_transform(make_tuple(AK0Number, Number<MLdsLayer>{})),
+                           make_pass_through_transform(Number<MPerBlock / MLdsLayer>{}),
+                           make_pass_through_transform(AK1Number)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}, Sequence<3>{}));
+
+            constexpr auto a_lds_block_desc_ak0_m_ak1 = transform_tensor_descriptor(
+                a_lds_block_desc_ak0_mldslayer_m_ak1,
+                make_tuple(make_pass_through_transform(AK0Number),
+                           make_merge_transform_v3_division_mod(
+                               make_tuple(Number<MPerBlock / MLdsLayer>{}, Number<MLdsLayer>{})),
+                           make_pass_through_transform(AK1Number)),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            return a_lds_block_desc_ak0_m_ak1;
+        }
+        else // ColumnMajor A
+        {
+            // kfold and mpair dimension is not always required.
+            // more dimension in merge_transform increase the difficulty of generating immarg offset
+            // for compiler.
+            constexpr auto M0 = ABlockTransferThreadClusterLengths_AK0_M_AK1{}.At(I1);
+            constexpr auto M1 = MPerBlock / M0;
+
+            constexpr auto KThreadWrite     = ABlockTransferThreadClusterLengths_AK0_M_AK1{}.At(I0);
+            constexpr auto K0PerThreadWrite = AK0Number / KThreadWrite;
+            constexpr auto KThreadRead      = 64 / MPerXdl;
+            constexpr auto K0PerThreadRead  = AK0Number / KThreadRead;
+
+            constexpr auto kfold = (AK1Number * M0 * sizeof(ADataType) > 128)
+                                       ? 1
+                                       : 128 / (AK1Number * M0 * sizeof(ADataType));
+            constexpr auto KThreadReadPerm =
+                (kfold * K0PerThreadWrite / K0PerThreadRead) > 1
+                    ? KThreadRead / (kfold * K0PerThreadWrite / K0PerThreadRead)
+                    : KThreadRead;
+
+            // 1<=mpair<=n0
+            constexpr auto mpair = (AK1Number * MPerXdl * sizeof(ADataType) > 128)
+                                       ? 1
+                                       : ((128 / (AK1Number * MPerXdl * sizeof(ADataType))) > M0
+                                              ? M0
+                                              : 128 / (AK1Number * MPerXdl * sizeof(ADataType)));
+
+            constexpr auto a_lds_block_desc = make_naive_tensor_descriptor_packed(
+                make_tuple(Number<KThreadWrite / kfold / KThreadReadPerm>{},
+                           Number<K0PerThreadWrite>{},
+                           Number<KThreadReadPerm * M1>{},
+                           Number<kfold * M0 / mpair>{},
+                           Number<mpair>{},
+                           AK1Number));
+
+            constexpr auto a_lds_block_desc_permuted = transform_tensor_descriptor(
+                a_lds_block_desc,
+                make_tuple(
+                    make_pass_through_transform(Number<KThreadWrite / kfold / KThreadReadPerm>{}),
+                    make_pass_through_transform(Number<K0PerThreadWrite>{}),
+                    make_xor_with_modulo_transform(
+                        make_tuple(Number<KThreadReadPerm * M1>{}, Number<kfold * M0 / mpair>{})),
+                    make_pass_through_transform(Number<mpair>{}),
+                    make_pass_through_transform(AK1Number)),
+                make_tuple(
+                    Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4>{}, Sequence<5>{}),
+                make_tuple(
+                    Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4>{}, Sequence<5>{}));
+
+            constexpr auto a_lds_block_desc_unmerged = transform_tensor_descriptor(
+                a_lds_block_desc_permuted,
+                make_tuple(
+                    make_pass_through_transform(Number<KThreadWrite / kfold / KThreadReadPerm>{}),
+                    make_pass_through_transform(Number<K0PerThreadWrite>{}),
+                    make_unmerge_transform(make_tuple(Number<KThreadReadPerm>{}, Number<M1>{})),
+                    make_unmerge_transform(make_tuple(Number<kfold>{}, Number<M0 / mpair>{})),
+                    make_pass_through_transform(Number<mpair>{}),
+                    make_pass_through_transform(AK1Number)),
+                make_tuple(Sequence<0>{},
+                           Sequence<1>{},
+                           Sequence<2>{},
+                           Sequence<3>{},
+                           Sequence<4>{},
+                           Sequence<5>{}),
+                make_tuple(Sequence<1>{},
+                           Sequence<2>{},
+                           Sequence<0, 3>{},
+                           Sequence<4, 5>{},
+                           Sequence<6>{},
+                           Sequence<7>{}));
+
+            constexpr auto a_lds_block_desc_ak0_m_ak1 = transform_tensor_descriptor(
+                a_lds_block_desc_unmerged,
+                make_tuple(make_merge_transform_v3_division_mod(
+                               make_tuple(Number<KThreadReadPerm>{},
+                                          Number<KThreadWrite / kfold / KThreadReadPerm>{},
+                                          Number<kfold>{},
+                                          Number<K0PerThreadWrite>{})),
+                           make_merge_transform_v3_division_mod(
+                               make_tuple(Number<M0 / mpair>{}, Number<mpair>{}, Number<M1>{})),
+                           make_pass_through_transform(AK1Number)),
+                make_tuple(Sequence<0, 1, 4, 2>{}, Sequence<5, 6, 3>{}, Sequence<7>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            return a_lds_block_desc_ak0_m_ak1;
+        }
+    }
+
+    __device__ static constexpr auto GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1()
+    {
+        // B matrix in LDS memory, dst of blockwise copy
+        if constexpr(BBlockLdsExtraN)
+        {
+            return make_naive_tensor_descriptor(
+                make_tuple(BK0Number, Number<NPerBlock>{}, BK1Number),
+                make_tuple(BK1Number, Number<KPerBlock + BBlockLdsExtraN>{}, I1));
+        }
+        else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
+        {
+            // NLdsLayer * K0 as logical Bank
+            constexpr auto NLdsLayer = 32 * 4 / KPerBlock / sizeof(BDataType) < 1
+                                           ? 1
+                                           : 32 * 4 / KPerBlock / sizeof(BDataType);
+            ;
+            constexpr auto b_lds_block_desc = make_naive_tensor_descriptor(
+                make_tuple(
+                    BK0Number * Number<NLdsLayer>{}, Number<NPerBlock / NLdsLayer>{}, BK1Number),
+                make_tuple(BK1Number, Number<KPerBlock * NLdsLayer>{}, I1));
+
+            constexpr auto b_lds_block_desc_permuted = transform_tensor_descriptor(
+                b_lds_block_desc,
+                make_tuple(make_xor_with_modulo_transform(make_tuple(
+                               Number<NPerBlock / NLdsLayer>{}, Number<BK0Number * NLdsLayer>{})),
+                           make_pass_through_transform(BK1Number)),
+                make_tuple(Sequence<1, 0>{}, Sequence<2>{}),
+                make_tuple(Sequence<1, 0>{}, Sequence<2>{}));
+
+            constexpr auto b_lds_block_desc_bk0_nldslayer_n_bk1 = transform_tensor_descriptor(
+                b_lds_block_desc_permuted,
+                make_tuple(make_unmerge_transform(make_tuple(BK0Number, Number<NLdsLayer>{})),
+                           make_pass_through_transform(Number<NPerBlock / NLdsLayer>{}),
+                           make_pass_through_transform(BK1Number)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}, Sequence<3>{}));
+
+            constexpr auto b_lds_block_desc_bk0_n_bk1 = transform_tensor_descriptor(
+                b_lds_block_desc_bk0_nldslayer_n_bk1,
+                make_tuple(make_pass_through_transform(BK0Number),
+                           make_merge_transform_v3_division_mod(
+                               make_tuple(Number<NPerBlock / NLdsLayer>{}, Number<NLdsLayer>{})),
+                           make_pass_through_transform(BK1Number)),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            return b_lds_block_desc_bk0_n_bk1;
+        }
+        else // RowMajor B
+        {
+            constexpr auto N0 = BBlockTransferThreadClusterLengths_BK0_N_BK1{}.At(I1);
+            constexpr auto N1 = NPerBlock / N0;
+
+            constexpr auto KThreadWrite     = BBlockTransferThreadClusterLengths_BK0_N_BK1{}.At(I0);
+            constexpr auto K0PerThreadWrite = BK0Number / KThreadWrite;
+            constexpr auto KThreadRead      = 64 / NPerXdl;
+            constexpr auto K0PerThreadRead  = BK0Number / KThreadRead;
+
+            constexpr auto kfold = (BK1Number * N0 * sizeof(BDataType) > 128)
+                                       ? 1
+                                       : 128 / (BK1Number * N0 * sizeof(BDataType));
+            constexpr auto KThreadReadPerm =
+                (kfold * K0PerThreadWrite / K0PerThreadRead) > 1
+                    ? KThreadRead / (kfold * K0PerThreadWrite / K0PerThreadRead)
+                    : KThreadRead;
+
+            // 1<=npair<=n0
+            constexpr auto npair = (BK1Number * NPerXdl * sizeof(BDataType) > 128)
+                                       ? 1
+                                       : ((128 / (BK1Number * NPerXdl * sizeof(BDataType))) > N0
+                                              ? N0
+                                              : 128 / (BK1Number * NPerXdl * sizeof(BDataType)));
+
+            constexpr auto b_lds_block_desc = make_naive_tensor_descriptor_packed(
+                make_tuple(Number<KThreadWrite / kfold / KThreadReadPerm>{},
+                           Number<K0PerThreadWrite>{},
+                           Number<KThreadReadPerm * N1>{},
+                           Number<kfold * N0 / npair>{},
+                           Number<npair>{},
+                           BK1Number));
+
+            constexpr auto b_lds_block_desc_permuted = transform_tensor_descriptor(
+                b_lds_block_desc,
+                make_tuple(
+                    make_pass_through_transform(Number<KThreadWrite / kfold / KThreadReadPerm>{}),
+                    make_pass_through_transform(Number<K0PerThreadWrite>{}),
+                    make_xor_with_modulo_transform(
+                        make_tuple(Number<KThreadReadPerm * N1>{}, Number<kfold * N0 / npair>{})),
+                    make_pass_through_transform(Number<npair>{}),
+                    make_pass_through_transform(BK1Number)),
+                make_tuple(
+                    Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4>{}, Sequence<5>{}),
+                make_tuple(
+                    Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4>{}, Sequence<5>{}));
+
+            constexpr auto b_lds_block_desc_unmerged = transform_tensor_descriptor(
+                b_lds_block_desc_permuted,
+                make_tuple(
+                    make_pass_through_transform(Number<KThreadWrite / kfold / KThreadReadPerm>{}),
+                    make_pass_through_transform(Number<K0PerThreadWrite>{}),
+                    make_unmerge_transform(make_tuple(Number<KThreadReadPerm>{}, Number<N1>{})),
+                    make_unmerge_transform(make_tuple(Number<kfold>{}, Number<N0 / npair>{})),
+                    make_pass_through_transform(Number<npair>{}),
+                    make_pass_through_transform(BK1Number)),
+                make_tuple(Sequence<0>{},
+                           Sequence<1>{},
+                           Sequence<2>{},
+                           Sequence<3>{},
+                           Sequence<4>{},
+                           Sequence<5>{}),
+                make_tuple(Sequence<1>{},
+                           Sequence<2>{},
+                           Sequence<0, 3>{},
+                           Sequence<4, 5>{},
+                           Sequence<6>{},
+                           Sequence<7>{}));
+
+            constexpr auto b_lds_block_desc_bk0_n_bk1 = transform_tensor_descriptor(
+                b_lds_block_desc_unmerged,
+                make_tuple(make_merge_transform_v3_division_mod(
+                               make_tuple(Number<KThreadReadPerm>{},
+                                          Number<KThreadWrite / kfold / KThreadReadPerm>{},
+                                          Number<kfold>{},
+                                          Number<K0PerThreadWrite>{})),
+                           make_merge_transform_v3_division_mod(
+                               make_tuple(Number<N0 / npair>{}, Number<npair>{}, Number<N1>{})),
+                           make_pass_through_transform(BK1Number)),
+                make_tuple(Sequence<0, 1, 4, 2>{}, Sequence<5, 6, 3>{}, Sequence<7>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            return b_lds_block_desc_bk0_n_bk1;
+        }
+    }
+
+    __device__ static constexpr auto GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock()
+    {
+        constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);
+        constexpr index_t NWave = NPerBlock / (NXdlPerWave * NPerXdl);
+
+        constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
+            make_naive_tensor_descriptor_packed(
+                make_tuple(I1,
+                           Number<CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl>{},
+                           I1,
+                           Number<CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>{}));
+
+        return c_shuffle_block_desc_mblock_mperblock_nblock_nperblock;
+    }
+
+    using BlockwiseGemmPipe =
+        remove_cvref_t<decltype(BlockGemmPipeline_Selector<
+                                BlkGemmPipelineVer,
+                                BlkGemmPipeSched,
+                                BlockSize,
+                                ADataType,
+                                BDataType,
+                                ComputeTypeA,
+                                AccDataType,
+                                decltype(GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1()),
+                                decltype(GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1()),
+                                decltype(MakeAMmaTileDescriptor_M0_M1_M2_K(
+                                    GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1())),
+                                decltype(MakeBMmaTileDescriptor_N0_N1_N2_K(
+                                    GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1())),
+                                ABlockTransferSrcScalarPerVector,
+                                BBlockTransferSrcScalarPerVector,
+                                MPerBlock,
+                                NPerBlock,
+                                KPerBlock,
+                                MPerXdl,
+                                NPerXdl,
+                                MXdlPerWave,
+                                NXdlPerWave,
+                                KPack>())>;
+
+    __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
+    {
+        // LDS allocation for A and B: be careful of alignment
+        constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
+        constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
+
+        // lds max alignment
+        constexpr auto max_lds_align = math::lcm(AK1Number, BK1Number);
+
+        constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
+            a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
+
+        constexpr auto b_block_space_size_aligned = math::integer_least_multiple(
+            b_block_desc_bk0_n_bk1.GetElementSpaceSize(), max_lds_align);
+
+        // LDS allocation for C shuffle in LDS
+        constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
+            GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
+
+        constexpr auto c_block_size =
+            c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize();
+
+        return math::max((a_block_space_size_aligned * sizeof(ADataType) +
+                          b_block_space_size_aligned * sizeof(BDataType)),
+                         c_block_size * sizeof(CShuffleDataType));
+    }
+
+    __host__ static constexpr bool CalculateHasMainKBlockLoop(index_t K)
+    {
+        const index_t num_loop = K / KPerBlock;
+
+        return BlockwiseGemmPipe::BlockHasHotloop(num_loop);
+    }
+
+    __host__ static constexpr TailNumber CalculateKBlockLoopTailNum(index_t K)
+    {
+        const index_t num_loop = K / KPerBlock;
+
+        return BlockwiseGemmPipe::BlockLoopTailNum(num_loop);
+    }
+
+    template <typename CGridDesc>
+    __host__ __device__ static constexpr auto MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
+        const CGridDesc& c_grid_desc_m_n, index_t MBlock, index_t NBlock)
+    {
+        const auto c_grid_desc_mblock_mperblock_nblock_nperblock = transform_tensor_descriptor(
+            c_grid_desc_m_n,
+            make_tuple(make_unmerge_transform(make_tuple(MBlock, Number<MPerBlock>{})),
+                       make_unmerge_transform(make_tuple(NBlock, Number<NPerBlock>{}))),
+            make_tuple(Sequence<0>{}, Sequence<1>{}),
+            make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));
+
+        return c_grid_desc_mblock_mperblock_nblock_nperblock;
+    }
+
+    // return block_id to C matrix tile idx (m0, n0) mapping
+    // if arch = gfx942
+    using Block2CTileMap = BlockToCTileMap_Grouped_M00_N0_M01Adapt<8, MPerBlock, NPerBlock>;
+
+    template <typename AGridDesc_AK0_M_K1,
+              typename BGridDesc_BK0_N_K1,
+              typename CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
+              bool HasMainKBlockLoop,
+              InMemoryDataOperationEnum CGlobalMemoryDataOperation,
+              TailNumber TailNum = TailNumber::Odd>
+    __device__ static void Run(const ADataType* p_a_grid,
+                               const BDataType* p_b_grid,
+                               CDataType* p_c_grid,
+                               void* p_shared,
+                               const Problem& problem,
+                               const AGridDesc_AK0_M_K1& a_grid_desc_ak0_m_ak1,
+                               const BGridDesc_BK0_N_K1& b_grid_desc_bk0_n_bk1,
+                               const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock&
+                                   c_grid_desc_mblock_mperblock_nblock_nperblock,
+                               const index_t k_id = 0)
+    {
+        const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_a_grid, a_grid_desc_ak0_m_ak1.GetElementSpaceSize());
+        const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_b_grid, b_grid_desc_bk0_n_bk1.GetElementSpaceSize());
+        auto c_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_c_grid, c_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
+
+        const AElementwiseOperation a_element_op{};
+        const BElementwiseOperation b_element_op{};
+        const CElementwiseOperation c_element_op{};
+
+        // divide block work by [M, N]
+        const auto block_2_ctile_map = Block2CTileMap{problem.M, problem.N, 4};
+
+        const auto block_work_idx = block_2_ctile_map.CalculateBottomIndex(
+            make_multi_index(static_cast<index_t>(blockIdx.x)));
+
+        if(!block_2_ctile_map.ValidCTileIndex(
+               block_work_idx,
+               make_tuple(c_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I0),
+                          c_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I2))))
+        {
+            return;
+        }
+
+        const index_t block_m_id = __builtin_amdgcn_readfirstlane(block_work_idx[I0]);
+        const index_t block_n_id = __builtin_amdgcn_readfirstlane(block_work_idx[I1]);
+
+        // HACK: this force m/n_block_data_idx_on_grid into SGPR
+        const index_t m_block_data_idx_on_grid =
+            __builtin_amdgcn_readfirstlane(block_m_id * MPerBlock);
+
+        const index_t n_block_data_idx_on_grid =
+            __builtin_amdgcn_readfirstlane(block_n_id * NPerBlock);
+
+        // lds max alignment
+        constexpr auto max_lds_align = math::lcm(AK1Number, BK1Number);
+
+        // A matrix in LDS memory, dst of blockwise copy
+        constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
+
+        // B matrix in LDS memory, dst of blockwise copy
+        constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
+
+        // A matrix blockwise copy
+        auto a_blockwise_copy =
+            ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
+                                                AElementwiseOperation,
+                                                ck::tensor_operation::element_wise::PassThrough,
+                                                InMemoryDataOperationEnum::Set,
+                                                Sequence<AK0Number, MPerBlock, AK1Number>,
+                                                ABlockTransferThreadClusterLengths_AK0_M_AK1,
+                                                ABlockTransferThreadClusterArrangeOrder,
+                                                ADataType,
+                                                ADataType,
+                                                decltype(a_grid_desc_ak0_m_ak1),
+                                                decltype(a_block_desc_ak0_m_ak1),
+                                                ABlockTransferSrcAccessOrder,
+                                                Sequence<0, 1, 2>,
+                                                ABlockTransferSrcVectorDim,
+                                                2,
+                                                ABlockTransferSrcScalarPerVector,
+                                                ABlockTransferDstScalarPerVector_AK1,
+                                                1,
+                                                1,
+                                                AThreadTransferSrcResetCoordinateAfterRun,
+                                                true,
+                                                BlockwiseGemmPipe::GlobalBufferNum>(
+                a_grid_desc_ak0_m_ak1,
+                make_multi_index(k_id, m_block_data_idx_on_grid, 0),
+                a_element_op,
+                a_block_desc_ak0_m_ak1,
+                make_multi_index(0, 0, 0),
+                ck::tensor_operation::element_wise::PassThrough{});
+
+        // B matrix blockwise copy
+        auto b_blockwise_copy =
+            ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
+                                                BElementwiseOperation,
+                                                ck::tensor_operation::element_wise::PassThrough,
+                                                InMemoryDataOperationEnum::Set,
+                                                Sequence<BK0Number, NPerBlock, BK1Number>,
+                                                BBlockTransferThreadClusterLengths_BK0_N_BK1,
+                                                BBlockTransferThreadClusterArrangeOrder,
+                                                BDataType,
+                                                BDataType,
+                                                decltype(b_grid_desc_bk0_n_bk1),
+                                                decltype(b_block_desc_bk0_n_bk1),
+                                                BBlockTransferSrcAccessOrder,
+                                                Sequence<0, 1, 2>,
+                                                BBlockTransferSrcVectorDim,
+                                                2,
+                                                BBlockTransferSrcScalarPerVector,
+                                                BBlockTransferDstScalarPerVector_BK1,
+                                                1,
+                                                1,
+                                                BThreadTransferSrcResetCoordinateAfterRun,
+                                                true,
+                                                BlockwiseGemmPipe::GlobalBufferNum>(
+                b_grid_desc_bk0_n_bk1,
+                make_multi_index(k_id, n_block_data_idx_on_grid, 0),
+                b_element_op,
+                b_block_desc_bk0_n_bk1,
+                make_multi_index(0, 0, 0),
+                ck::tensor_operation::element_wise::PassThrough{});
+
+        // LDS allocation for A and B: be careful of alignment
+        constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
+            a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
+
+        // Cast after lds
+        auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+            static_cast<ADataType*>(p_shared), a_block_desc_ak0_m_ak1.GetElementSpaceSize());
+
+        auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+            static_cast<BDataType*>(p_shared) +
+                a_block_space_size_aligned * sizeof(ADataType) / sizeof(BDataType),
+            b_block_desc_bk0_n_bk1.GetElementSpaceSize());
+
+        constexpr auto a_block_slice_copy_step = make_multi_index(KPerBlock / AK1Number, 0, 0);
+        constexpr auto b_block_slice_copy_step = make_multi_index(KPerBlock / BK1Number, 0, 0);
+
+        // Blockwise GEMM pipeline
+        static_assert(std::is_default_constructible_v<BlockwiseGemmPipe>);
+        auto blockwise_gemm_pipeline = BlockwiseGemmPipe{};
+        auto c_thread_buf            = blockwise_gemm_pipeline.GetCThreadBuffer();
+
+        const index_t num_k_block_main_loop = __builtin_amdgcn_readfirstlane(
+            (a_grid_desc_ak0_m_ak1.GetLength(I0) * a_grid_desc_ak0_m_ak1.GetLength(I2)) /
+            (KPerBlock * problem.KBatch));
+
+        blockwise_gemm_pipeline.template Run<HasMainKBlockLoop, TailNum>(a_grid_desc_ak0_m_ak1,
+                                                                         a_block_desc_ak0_m_ak1,
+                                                                         a_blockwise_copy,
+                                                                         a_grid_buf,
+                                                                         a_block_buf,
+                                                                         a_block_slice_copy_step,
+                                                                         b_grid_desc_bk0_n_bk1,
+                                                                         b_block_desc_bk0_n_bk1,
+                                                                         b_blockwise_copy,
+                                                                         b_grid_buf,
+                                                                         b_block_buf,
+                                                                         b_block_slice_copy_step,
+                                                                         c_thread_buf,
+                                                                         num_k_block_main_loop);
+
+        // shuffle C and write out
+        {
+            static_assert(MXdlPerWave % CShuffleMXdlPerWavePerShuffle == 0 &&
+                              NXdlPerWave % CShuffleNXdlPerWavePerShuffle == 0,
+                          "wrong!");
+
+            constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);
+            constexpr index_t NWave = NPerBlock / (NXdlPerWave * NPerXdl);
+
+            // TODO: hacky, fix it!
+            constexpr auto c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2 =
+                blockwise_gemm_pipeline.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
+
+            // TODO: hacky, fix it!
+            // c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp is only used to get lengths
+            constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp =
+                blockwise_gemm_pipeline.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
+
+            constexpr auto M0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I0);
+            constexpr auto N0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I1);
+            constexpr auto M1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I2);
+            constexpr auto N1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I3);
+            constexpr auto M2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I4);
+            constexpr auto M3 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I5);
+            constexpr auto M4 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I6);
+            constexpr auto N2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I7);
+
+            constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
+                GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
+
+            auto c_shuffle_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+                static_cast<CShuffleDataType*>(p_shared),
+                c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
+
+            constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2 = transform_tensor_descriptor(
+                c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
+                make_tuple(
+                    make_freeze_transform(I0),
+                    make_unmerge_transform(make_tuple(
+                        Number<CShuffleMXdlPerWavePerShuffle>{}, // M0 (MXdlPerWave) per shuffle
+                        M1,                                      // M1 = MWave
+                        M2,                                      // M2 * M3 * M4 = MPerXdl
+                        M3,
+                        M4)),
+                    make_freeze_transform(I0),
+                    make_unmerge_transform(make_tuple(
+                        Number<CShuffleNXdlPerWavePerShuffle>{}, // N0 (NXdlPerWave) per shuffle
+                        N1,                                      // N1 = NWave
+                        N2))),                                   // N2 = NPerXdl
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(
+                    Sequence<>{}, Sequence<0, 2, 4, 5, 6>{}, Sequence<>{}, Sequence<1, 3, 7>{}));
+
+            // calculate origin of thread output tensor on global memory
+            //     blockwise GEMM c matrix starting index
+            const auto c_thread_mtx_on_block =
+                blockwise_gemm_pipeline.CalculateCThreadOriginDataIndex(I0, I0, I0, I0);
+
+            const index_t m_thread_data_on_block = c_thread_mtx_on_block[I0];
+            const index_t n_thread_data_on_block = c_thread_mtx_on_block[I1];
+
+            const auto m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor =
+                make_single_stage_tensor_adaptor(
+                    make_tuple(make_merge_transform(make_tuple(M0, M1, M2, M3, M4))),
+                    make_tuple(Sequence<0, 1, 2, 3, 4>{}),
+                    make_tuple(Sequence<0>{}));
+
+            const auto m_thread_data_on_block_idx =
+                m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor.CalculateBottomIndex(
+                    make_multi_index(m_thread_data_on_block));
+
+            const auto n_thread_data_on_block_to_n0_n1_n2_adaptor =
+                make_single_stage_tensor_adaptor(
+                    make_tuple(make_merge_transform(make_tuple(N0, N1, N2))),
+                    make_tuple(Sequence<0, 1, 2>{}),
+                    make_tuple(Sequence<0>{}));
+
+            const auto n_thread_data_on_block_idx =
+                n_thread_data_on_block_to_n0_n1_n2_adaptor.CalculateBottomIndex(
+                    make_multi_index(n_thread_data_on_block));
+
+            // shuffle: threadwise copy C from VGPR to LDS
+            auto c_thread_copy_vgpr_to_lds =
+                ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
+                                                   CShuffleDataType,
+                                                   decltype(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2),
+                                                   decltype(c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2),
+                                                   ck::tensor_operation::element_wise::PassThrough,
+                                                   Sequence<CShuffleMXdlPerWavePerShuffle,
+                                                            CShuffleNXdlPerWavePerShuffle,
+                                                            I1,
+                                                            I1,
+                                                            M2,
+                                                            I1,
+                                                            M4,
+                                                            I1>,
+                                                   Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
+                                                   7,
+                                                   1,
+                                                   InMemoryDataOperationEnum::Set,
+                                                   1,
+                                                   true>{
+                    c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
+                    make_multi_index(0,
+                                     0,
+                                     m_thread_data_on_block_idx[I1],
+                                     n_thread_data_on_block_idx[I1],
+                                     m_thread_data_on_block_idx[I2],
+                                     m_thread_data_on_block_idx[I3],
+                                     m_thread_data_on_block_idx[I4],
+                                     n_thread_data_on_block_idx[I2]),
+                    ck::tensor_operation::element_wise::PassThrough{}};
+
+            // shuffle: blockwise copy C from LDS to global
+            auto c_shuffle_block_copy_lds_to_global = ThreadGroupTensorSliceTransfer_v6r1<
+                ThisThreadBlock,            // ThreadGroup
+                CElementwiseOperation,      // ElementwiseOperation,
+                CGlobalMemoryDataOperation, // DstInMemOp,
+                Sequence<1,
+                         CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
+                         1,
+                         CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>, // BlockSliceLengths,
+                CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+                Sequence<0, 1, 2, 3>, // typename ThreadClusterArrangeOrder,
+                CShuffleDataType,     // typename SrcData,
+                CDataType,            // typename DstData,
+                decltype(c_shuffle_block_desc_mblock_mperblock_nblock_nperblock),
+                decltype(c_grid_desc_mblock_mperblock_nblock_nperblock),
+                Sequence<0, 1, 2, 3>,                           // typename DimAccessOrder,
+                3,                                              // index_t VectorDim,
+                CShuffleBlockTransferScalarPerVector_NPerBlock, // index_t ScalarPerVector,
+                true,  // bool ThreadTransferSrcResetCoordinateAfterRun,
+                false> // bool ThreadTransferDstResetCoordinateAfterRun>
+                {c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
+                 make_multi_index(0, 0, 0, 0),
+                 c_grid_desc_mblock_mperblock_nblock_nperblock,
+                 make_multi_index(block_m_id, 0, block_n_id, 0),
+                 c_element_op};
+
+            // space filling curve for threadwise C in VGPR
+            constexpr auto sfc_c_vgpr =
+                SpaceFillingCurve<Sequence<MXdlPerWave, NXdlPerWave, 1, 1, M2, 1, M4, 1>,
+                                  Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
+                                  Sequence<CShuffleMXdlPerWavePerShuffle,
+                                           CShuffleNXdlPerWavePerShuffle,
+                                           1,
+                                           1,
+                                           M2,
+                                           1,
+                                           M4,
+                                           1>>{};
+
+            // space filling curve for shuffled blockwise C in global mem
+            constexpr auto sfc_c_global =
+                SpaceFillingCurve<Sequence<1, MPerBlock, 1, NPerBlock>,
+                                  Sequence<0, 2, 1, 3>,
+                                  Sequence<1,
+                                           CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
+                                           1,
+                                           CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>>{};
+
+            constexpr index_t num_access = sfc_c_vgpr.GetNumOfAccess();
+
+            static_assert(num_access == sfc_c_global.GetNumOfAccess(), "wrong!");
+
+            static_for<0, num_access, 1>{}([&](auto access_id) {
+                // make sure it's safe to write to LDS
+                block_sync_lds();
+
+                // each thread write its data from VGPR to LDS
+                c_thread_copy_vgpr_to_lds.Run(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2,
+                                              sfc_c_vgpr.GetIndexTupleOfNumber(access_id),
+                                              c_thread_buf,
+                                              c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
+                                              c_shuffle_block_buf);
+
+                // make sure it's safe to read from LDS
+                block_sync_lds();
+
+                // each block copy its data from LDS to global
+                c_shuffle_block_copy_lds_to_global.Run(
+                    c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
+                    c_shuffle_block_buf,
+                    c_grid_desc_mblock_mperblock_nblock_nperblock,
+                    c_grid_buf);
+
+                if constexpr(access_id < num_access - 1)
+                {
+                    constexpr auto c_global_step = sfc_c_global.GetForwardStep(access_id);
+
+                    // move on C
+                    c_shuffle_block_copy_lds_to_global.MoveDstSliceWindow(
+                        c_grid_desc_mblock_mperblock_nblock_nperblock, c_global_step);
+                }
+            });
+        }
+    }
+
+    template <typename AGridDesc_AK0_M_K1,
+              typename BGridDesc_BK0_N_K1,
+              typename CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
+              bool HasMainKBlockLoop,
+              InMemoryDataOperationEnum CGlobalMemoryDataOperation,
+              TailNumber TailNum = TailNumber::Odd>
+    __device__ static void Run_2Lds(const ADataType* p_a_grid,
+                                    const BDataType* p_b_grid,
+                                    CDataType* p_c_grid,
+                                    void* p_shared_0,
+                                    void* p_shared_1,
+                                    const Problem& problem,
+                                    const AGridDesc_AK0_M_K1& a_grid_desc_ak0_m_ak1,
+                                    const BGridDesc_BK0_N_K1& b_grid_desc_bk0_n_bk1,
+                                    const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock&
+                                        c_grid_desc_mblock_mperblock_nblock_nperblock,
+                                    const index_t k_id = 0)
+    {
+        const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_a_grid, a_grid_desc_ak0_m_ak1.GetElementSpaceSize());
+        const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_b_grid, b_grid_desc_bk0_n_bk1.GetElementSpaceSize());
+        auto c_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_c_grid, c_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
+
+        const AElementwiseOperation a_element_op{};
+        const BElementwiseOperation b_element_op{};
+        const CElementwiseOperation c_element_op{};
+
+        // divide block work by [M, N]
+        const auto block_2_ctile_map = Block2CTileMap{problem.M, problem.N, 4};
+
+        const auto block_work_idx = block_2_ctile_map.CalculateBottomIndex(
+            make_multi_index(static_cast<index_t>(blockIdx.x)));
+
+        if(!block_2_ctile_map.ValidCTileIndex(
+               block_work_idx,
+               make_tuple(c_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I0),
+                          c_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I2))))
+        {
+            return;
+        }
+
+        const index_t block_m_id = __builtin_amdgcn_readfirstlane(block_work_idx[I0]);
+        const index_t block_n_id = __builtin_amdgcn_readfirstlane(block_work_idx[I1]);
+
+        // HACK: this force m/n_block_data_idx_on_grid into SGPR
+        const index_t m_block_data_idx_on_grid =
+            __builtin_amdgcn_readfirstlane(block_m_id * MPerBlock);
+
+        const index_t n_block_data_idx_on_grid =
+            __builtin_amdgcn_readfirstlane(block_n_id * NPerBlock);
+
+        // lds max alignment
+        constexpr auto max_lds_align = math::lcm(AK1Number, BK1Number);
+
+        // A matrix in LDS memory, dst of blockwise copy
+        constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
+
+        // B matrix in LDS memory, dst of blockwise copy
+        constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
+
+        // A matrix blockwise copy
+        auto a_blockwise_copy =
+            ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
+                                                AElementwiseOperation,
+                                                ck::tensor_operation::element_wise::PassThrough,
+                                                InMemoryDataOperationEnum::Set,
+                                                Sequence<AK0Number, MPerBlock, AK1Number>,
+                                                ABlockTransferThreadClusterLengths_AK0_M_AK1,
+                                                ABlockTransferThreadClusterArrangeOrder,
+                                                ADataType,
+                                                ADataType,
+                                                decltype(a_grid_desc_ak0_m_ak1),
+                                                decltype(a_block_desc_ak0_m_ak1),
+                                                ABlockTransferSrcAccessOrder,
+                                                Sequence<0, 1, 2>,
+                                                ABlockTransferSrcVectorDim,
+                                                2,
+                                                ABlockTransferSrcScalarPerVector,
+                                                ABlockTransferDstScalarPerVector_AK1,
+                                                1,
+                                                1,
+                                                AThreadTransferSrcResetCoordinateAfterRun,
+                                                true,
+                                                BlockwiseGemmPipe::GlobalBufferNum>(
+                a_grid_desc_ak0_m_ak1,
+                make_multi_index(k_id, m_block_data_idx_on_grid, 0),
+                a_element_op,
+                a_block_desc_ak0_m_ak1,
+                make_multi_index(0, 0, 0),
+                ck::tensor_operation::element_wise::PassThrough{});
+
+        // B matrix blockwise copy
+        auto b_blockwise_copy =
+            ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
+                                                BElementwiseOperation,
+                                                ck::tensor_operation::element_wise::PassThrough,
+                                                InMemoryDataOperationEnum::Set,
+                                                Sequence<BK0Number, NPerBlock, BK1Number>,
+                                                BBlockTransferThreadClusterLengths_BK0_N_BK1,
+                                                BBlockTransferThreadClusterArrangeOrder,
+                                                BDataType,
+                                                BDataType,
+                                                decltype(b_grid_desc_bk0_n_bk1),
+                                                decltype(b_block_desc_bk0_n_bk1),
+                                                BBlockTransferSrcAccessOrder,
+                                                Sequence<0, 1, 2>,
+                                                BBlockTransferSrcVectorDim,
+                                                2,
+                                                BBlockTransferSrcScalarPerVector,
+                                                BBlockTransferDstScalarPerVector_BK1,
+                                                1,
+                                                1,
+                                                BThreadTransferSrcResetCoordinateAfterRun,
+                                                true,
+                                                BlockwiseGemmPipe::GlobalBufferNum>(
+                b_grid_desc_bk0_n_bk1,
+                make_multi_index(k_id, n_block_data_idx_on_grid, 0),
+                b_element_op,
+                b_block_desc_bk0_n_bk1,
+                make_multi_index(0, 0, 0),
+                ck::tensor_operation::element_wise::PassThrough{});
+
+        // LDS allocation for A and B: be careful of alignment
+        constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
+            a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
+
+        auto a_block_buf_ping = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+            static_cast<ADataType*>(p_shared_0), a_block_desc_ak0_m_ak1.GetElementSpaceSize());
+
+        auto b_block_buf_ping = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+            static_cast<BDataType*>(p_shared_0) +
+                a_block_space_size_aligned * sizeof(ADataType) / sizeof(BDataType),
+            b_block_desc_bk0_n_bk1.GetElementSpaceSize());
+
+        auto a_block_buf_pong = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+            static_cast<ADataType*>(p_shared_1), a_block_desc_ak0_m_ak1.GetElementSpaceSize());
+
+        auto b_block_buf_pong = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+            static_cast<BDataType*>(p_shared_1) +
+                a_block_space_size_aligned * sizeof(ADataType) / sizeof(BDataType),
+            b_block_desc_bk0_n_bk1.GetElementSpaceSize());
+
+        auto a_block_bufs = make_tuple(a_block_buf_ping, a_block_buf_pong);
+        auto b_block_bufs = make_tuple(b_block_buf_ping, b_block_buf_pong);
+
+        constexpr auto a_block_slice_copy_step = make_multi_index(KPerBlock / AK1Number, 0, 0);
+        constexpr auto b_block_slice_copy_step = make_multi_index(KPerBlock / BK1Number, 0, 0);
+
+        // Blockwise GEMM pipeline
+        static_assert(std::is_default_constructible_v<BlockwiseGemmPipe>);
+        auto blockwise_gemm_pipeline = BlockwiseGemmPipe{};
+        auto c_thread_buf            = blockwise_gemm_pipeline.GetCThreadBuffer();
+
+        const index_t num_k_block_main_loop = __builtin_amdgcn_readfirstlane(
+            (a_grid_desc_ak0_m_ak1.GetLength(I0) * a_grid_desc_ak0_m_ak1.GetLength(I2)) /
+            (KPerBlock * problem.KBatch));
+
+        blockwise_gemm_pipeline.template Run<HasMainKBlockLoop, TailNum>(a_grid_desc_ak0_m_ak1,
+                                                                         a_block_desc_ak0_m_ak1,
+                                                                         a_blockwise_copy,
+                                                                         a_grid_buf,
+                                                                         a_block_bufs,
+                                                                         a_block_slice_copy_step,
+                                                                         b_grid_desc_bk0_n_bk1,
+                                                                         b_block_desc_bk0_n_bk1,
+                                                                         b_blockwise_copy,
+                                                                         b_grid_buf,
+                                                                         b_block_bufs,
+                                                                         b_block_slice_copy_step,
+                                                                         c_thread_buf,
+                                                                         num_k_block_main_loop);
+
+        // shuffle C and write out
+        {
+            static_assert(MXdlPerWave % CShuffleMXdlPerWavePerShuffle == 0 &&
+                              NXdlPerWave % CShuffleNXdlPerWavePerShuffle == 0,
+                          "wrong!");
+
+            constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);
+            constexpr index_t NWave = NPerBlock / (NXdlPerWave * NPerXdl);
+
+            // TODO: hacky, fix it!
+            constexpr auto c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2 =
+                blockwise_gemm_pipeline.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
+
+            // TODO: hacky, fix it!
+            // c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp is only used to get lengths
+            constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp =
+                blockwise_gemm_pipeline.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
+
+            constexpr auto M0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I0);
+            constexpr auto N0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I1);
+            constexpr auto M1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I2);
+            constexpr auto N1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I3);
+            constexpr auto M2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I4);
+            constexpr auto M3 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I5);
+            constexpr auto M4 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I6);
+            constexpr auto N2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I7);
+
+            constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
+                GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
+
+            auto c_shuffle_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+                static_cast<CShuffleDataType*>(p_shared_0),
+                c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
+
+            constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2 = transform_tensor_descriptor(
+                c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
+                make_tuple(
+                    make_freeze_transform(I0),
+                    make_unmerge_transform(make_tuple(
+                        Number<CShuffleMXdlPerWavePerShuffle>{}, // M0 (MXdlPerWave) per shuffle
+                        M1,                                      // M1 = MWave
+                        M2,                                      // M2 * M3 * M4 = MPerXdl
+                        M3,
+                        M4)),
+                    make_freeze_transform(I0),
+                    make_unmerge_transform(make_tuple(
+                        Number<CShuffleNXdlPerWavePerShuffle>{}, // N0 (NXdlPerWave) per shuffle
+                        N1,                                      // N1 = NWave
+                        N2))),                                   // N2 = NPerXdl
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(
+                    Sequence<>{}, Sequence<0, 2, 4, 5, 6>{}, Sequence<>{}, Sequence<1, 3, 7>{}));
+
+            // calculate origin of thread output tensor on global memory
+            //     blockwise GEMM c matrix starting index
+            const auto c_thread_mtx_on_block =
+                blockwise_gemm_pipeline.CalculateCThreadOriginDataIndex(I0, I0, I0, I0);
+
+            const index_t m_thread_data_on_block = c_thread_mtx_on_block[I0];
+            const index_t n_thread_data_on_block = c_thread_mtx_on_block[I1];
+
+            const auto m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor =
+                make_single_stage_tensor_adaptor(
+                    make_tuple(make_merge_transform(make_tuple(M0, M1, M2, M3, M4))),
+                    make_tuple(Sequence<0, 1, 2, 3, 4>{}),
+                    make_tuple(Sequence<0>{}));
+
+            const auto m_thread_data_on_block_idx =
+                m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor.CalculateBottomIndex(
+                    make_multi_index(m_thread_data_on_block));
+
+            const auto n_thread_data_on_block_to_n0_n1_n2_adaptor =
+                make_single_stage_tensor_adaptor(
+                    make_tuple(make_merge_transform(make_tuple(N0, N1, N2))),
+                    make_tuple(Sequence<0, 1, 2>{}),
+                    make_tuple(Sequence<0>{}));
+
+            const auto n_thread_data_on_block_idx =
+                n_thread_data_on_block_to_n0_n1_n2_adaptor.CalculateBottomIndex(
+                    make_multi_index(n_thread_data_on_block));
+
+            // shuffle: threadwise copy C from VGPR to LDS
+            auto c_thread_copy_vgpr_to_lds =
+                ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
+                                                   CShuffleDataType,
+                                                   decltype(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2),
+                                                   decltype(c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2),
+                                                   ck::tensor_operation::element_wise::PassThrough,
+                                                   Sequence<CShuffleMXdlPerWavePerShuffle,
+                                                            CShuffleNXdlPerWavePerShuffle,
+                                                            I1,
+                                                            I1,
+                                                            M2,
+                                                            I1,
+                                                            M4,
+                                                            I1>,
+                                                   Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
+                                                   7,
+                                                   1,
+                                                   InMemoryDataOperationEnum::Set,
+                                                   1,
+                                                   true>{
+                    c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
+                    make_multi_index(0,
+                                     0,
+                                     m_thread_data_on_block_idx[I1],
+                                     n_thread_data_on_block_idx[I1],
+                                     m_thread_data_on_block_idx[I2],
+                                     m_thread_data_on_block_idx[I3],
+                                     m_thread_data_on_block_idx[I4],
+                                     n_thread_data_on_block_idx[I2]),
+                    ck::tensor_operation::element_wise::PassThrough{}};
+
+            // shuffle: blockwise copy C from LDS to global
+            auto c_shuffle_block_copy_lds_to_global = ThreadGroupTensorSliceTransfer_v6r1<
+                ThisThreadBlock,            // ThreadGroup
+                CElementwiseOperation,      // ElementwiseOperation,
+                CGlobalMemoryDataOperation, // DstInMemOp,
+                Sequence<1,
+                         CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
+                         1,
+                         CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>, // BlockSliceLengths,
+                CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+                Sequence<0, 1, 2, 3>, // typename ThreadClusterArrangeOrder,
+                CShuffleDataType,     // typename SrcData,
+                CDataType,            // typename DstData,
+                decltype(c_shuffle_block_desc_mblock_mperblock_nblock_nperblock),
+                decltype(c_grid_desc_mblock_mperblock_nblock_nperblock),
+                Sequence<0, 1, 2, 3>,                           // typename DimAccessOrder,
+                3,                                              // index_t VectorDim,
+                CShuffleBlockTransferScalarPerVector_NPerBlock, // index_t ScalarPerVector,
+                true,  // bool ThreadTransferSrcResetCoordinateAfterRun,
+                false> // bool ThreadTransferDstResetCoordinateAfterRun>
+                {c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
+                 make_multi_index(0, 0, 0, 0),
+                 c_grid_desc_mblock_mperblock_nblock_nperblock,
+                 make_multi_index(block_m_id, 0, block_n_id, 0),
+                 c_element_op};
+
+            // space filling curve for threadwise C in VGPR
+            constexpr auto sfc_c_vgpr =
+                SpaceFillingCurve<Sequence<MXdlPerWave, NXdlPerWave, 1, 1, M2, 1, M4, 1>,
+                                  Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
+                                  Sequence<CShuffleMXdlPerWavePerShuffle,
+                                           CShuffleNXdlPerWavePerShuffle,
+                                           1,
+                                           1,
+                                           M2,
+                                           1,
+                                           M4,
+                                           1>>{};
+
+            // space filling curve for shuffled blockwise C in global mem
+            constexpr auto sfc_c_global =
+                SpaceFillingCurve<Sequence<1, MPerBlock, 1, NPerBlock>,
+                                  Sequence<0, 2, 1, 3>,
+                                  Sequence<1,
+                                           CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
+                                           1,
+                                           CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>>{};
+
+            constexpr index_t num_access = sfc_c_vgpr.GetNumOfAccess();
+
+            static_assert(num_access == sfc_c_global.GetNumOfAccess(), "wrong!");
+
+            static_for<0, num_access, 1>{}([&](auto access_id) {
+                // make sure it's safe to write to LDS
+                block_sync_lds();
+
+                // each thread write its data from VGPR to LDS
+                c_thread_copy_vgpr_to_lds.Run(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2,
+                                              sfc_c_vgpr.GetIndexTupleOfNumber(access_id),
+                                              c_thread_buf,
+                                              c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
+                                              c_shuffle_block_buf);
+
+                // make sure it's safe to read from LDS
+                block_sync_lds();
+
+                // each block copy its data from LDS to global
+                c_shuffle_block_copy_lds_to_global.Run(
+                    c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
+                    c_shuffle_block_buf,
+                    c_grid_desc_mblock_mperblock_nblock_nperblock,
+                    c_grid_buf);
+
+                if constexpr(access_id < num_access - 1)
+                {
+                    constexpr auto c_global_step = sfc_c_global.GetForwardStep(access_id);
+
+                    // move on C
+                    c_shuffle_block_copy_lds_to_global.MoveDstSliceWindow(
+                        c_grid_desc_mblock_mperblock_nblock_nperblock, c_global_step);
+                }
+            });
+        }
+    }
+};
+
+} // namespace ck

include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdl_cshuffle_v3.hpp

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
 
@@ -34,8 +34,7 @@ __global__ void
     // __attribute__((amdgpu_waves_per_eu(1, 1)))
     kernel_gemm_xdl_cshuffle_v3(typename GridwiseGemm::Argument karg)
 {
-#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
-    defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__))
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx9__))
     __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
 
     auto splitk_batch_offset = typename GridwiseGemm::SplitKBatchOffset(karg);
@@ -48,7 +47,7 @@ __global__ void
         karg);
 #else
     ignore = karg;
-#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
+#endif // end of if (defined(__gfx9__))
 }
 
 template <typename GridwiseGemm,
@@ -63,8 +62,7 @@ __global__ void
     // __attribute__((amdgpu_waves_per_eu(1, 1)))
     kernel_gemm_xdl_cshuffle_v3_2lds(typename GridwiseGemm::Argument karg)
 {
-#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
-    defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__))
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx9__))
     // Pass two lds pointer is the key to tell compiler that ds_read/write
     // operate on different lds chunk at same time without order dependecy
     __shared__ char p_shared_0[GridwiseGemm::GetSharedMemoryNumberOfByte()];
@@ -81,7 +79,7 @@ __global__ void
         karg);
 #else
     ignore = karg;
-#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
+#endif // end of if (defined(__gfx9__))
 }
 
 template <typename ALayout,
@@ -605,8 +603,8 @@ struct GridwiseGemm_xdl_cshuffle_v3
 
             constexpr auto a_lds_block_desc_permuted = transform_tensor_descriptor(
                 a_lds_block_desc,
-                make_tuple(make_xor_transform(make_tuple(Number<MPerBlock / MLdsLayer>{},
-                                                         Number<AK0Number * MLdsLayer>{})),
+                make_tuple(make_xor_with_modulo_transform(make_tuple(
+                               Number<MPerBlock / MLdsLayer>{}, Number<AK0Number * MLdsLayer>{})),
                            make_pass_through_transform(AK1Number)),
                 make_tuple(Sequence<1, 0>{}, Sequence<2>{}),
                 make_tuple(Sequence<1, 0>{}, Sequence<2>{}));
@@ -671,7 +669,7 @@ struct GridwiseGemm_xdl_cshuffle_v3
                 make_tuple(
                     make_pass_through_transform(Number<KThreadWrite / kfold / KThreadReadPerm>{}),
                     make_pass_through_transform(Number<K0PerThreadWrite>{}),
-                    make_xor_transform(
+                    make_xor_with_modulo_transform(
                         make_tuple(Number<KThreadReadPerm * M1>{}, Number<kfold * M0 / mpair>{})),
                     make_pass_through_transform(Number<mpair>{}),
                     make_pass_through_transform(AK1Number)),
@@ -742,8 +740,8 @@ struct GridwiseGemm_xdl_cshuffle_v3
 
             constexpr auto b_lds_block_desc_permuted = transform_tensor_descriptor(
                 b_lds_block_desc,
-                make_tuple(make_xor_transform(make_tuple(Number<NPerBlock / NLdsLayer>{},
-                                                         Number<BK0Number * NLdsLayer>{})),
+                make_tuple(make_xor_with_modulo_transform(make_tuple(
+                               Number<NPerBlock / NLdsLayer>{}, Number<BK0Number * NLdsLayer>{})),
                            make_pass_through_transform(BK1Number)),
                 make_tuple(Sequence<1, 0>{}, Sequence<2>{}),
                 make_tuple(Sequence<1, 0>{}, Sequence<2>{}));
@@ -805,7 +803,7 @@ struct GridwiseGemm_xdl_cshuffle_v3
                 make_tuple(
                     make_pass_through_transform(Number<KThreadWrite / kfold / KThreadReadPerm>{}),
                     make_pass_through_transform(Number<K0PerThreadWrite>{}),
-                    make_xor_transform(
+                    make_xor_with_modulo_transform(
                         make_tuple(Number<KThreadReadPerm * N1>{}, Number<kfold * N0 / npair>{})),
                     make_pass_through_transform(Number<npair>{}),
                     make_pass_through_transform(BK1Number)),